Merge branch 'develop' into wraper_for_l2_normalize

doc/api/v2/fluid/layers.rst

@@ -364,6 +364,12 @@ split
 ..  autofunction:: paddle.v2.fluid.layers.split
     :noindex:
 
+
+matmul
+------
+..  autofunction:: paddle.v2.fluid.layers.matmul
+    :noindex:
+
 logsigmoid
 ----------
 ..  autofunction:: paddle.v2.fluid.layers.logsigmoid

doc/api/v2/fluid/nets.rst

@@ -25,3 +25,9 @@ glu
 ..  autofunction:: paddle.v2.fluid.nets.glu
     :noindex:
 
+
+dot_product_attention
+---------------------
+..  autofunction:: paddle.v2.fluid.nets.dot_product_attention
+    :noindex:
+

doc/howto/usage/capi/organization_of_the_inputs_cn.md

@@ -19,7 +19,7 @@
 
 ### 基本使用概念
 
-- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输入，每一个输入/输入都会对应有自己的`Argument`。
+- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
 - `Argument` 并不真正“存储”数据，而是将输入/输出信息有机地组织在一起。
 - 在`Argument`内部由`IVector`（对应着上文提到的一维整型数组）和`Matrix`（对应着上文提到的二维浮点型矩阵）来实际存储数据；由 `Sequence Start Positions` (下文详细解释) 来描述输入/输出的序列信息。
 

paddle/framework/lod_tensor.cc

@@ -135,6 +135,65 @@ bool operator==(const LoD &a, const LoD &b) {
   return true;
 }
 
+bool CheckLoD(const LoD &in, int tensor_height) {
+  if (in.empty()) return true;
+  for (const auto &level : in) {
+    // check: there should be more than 2 offsets existing in each level.
+    if (level.size() < 2) return false;
+    // check: the first offset(the begin offset) of each level should be 0.
+    if (level.front() != 0) return false;
+    // check: all the offsets in a level should be ascending(no same items
+    // allows).
+    if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
+          if (a < b) return true;
+          return false;
+        })) {
+      LOG(INFO) << "ascending error";
+      return false;
+    }
+  }
+  // check: the lowest level's last offset should equals `tensor_height` if
+  //        tensor_height>0.
+  if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
+    return false;
+
+  // check: the higher level's last offset should equals the lower level's
+  // size-1.
+  // NOTE LoD store the levels from top to bottom, so the higher level goes
+  // first.
+  for (size_t level = 0; level < in.size() - 1; level++) {
+    if (in[level].back() != in[level + 1].size() - 1) return false;
+  }
+  return true;
+}
+
+bool CheckAbsLoD(const LoD &in, int tensor_height) {
+  if (in.empty()) return true;
+  for (const auto &level : in) {
+    // check: all the offsets in a level should be ascending(no same items
+    // allows).
+    if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
+          if (a < b) return true;
+          return false;
+        })) {
+      return false;
+    }
+
+    // check: there should be more than 2 offsets existing in each level.
+    if (level.size() < 2) return false;
+
+    // check: the first offset of each level should be 0, and the last should be
+    // the same(the height of underlying tensor).
+    if (level.front() != 0) return false;
+    if (tensor_height < 0) {
+      tensor_height = level.back();
+    } else if ((size_t)tensor_height != level.back()) {
+      return false;
+    }
+  }
+  return true;
+}
+
 using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
 LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
                                         size_t end_idx, size_t start_level) {

paddle/framework/lod_tensor.h

@@ -71,6 +71,38 @@ LoD ToAbsOffset(const LoD& in);
 
 bool operator==(const LoD& a, const LoD& b);
 
+/*
+ * Check whether this lod's format is valid.
+ *
+ * ATTENTION:
+ *   - Empty lod is treated as valid.
+ *
+ * It will check two things:
+ *
+ *  1. all the offsets in a level should be ascending(no same items allows).
+ *  2. there should be more than 2 offsets existing in each level.
+ *  3. the higher level's last offset should equals the lower level's size-1.
+ *  4. the first offset(the begin offset) of each level should be 0.
+ *  5. the lowest level's last offset should equals `tensor_height` if
+ * tensor_height>0.
+ */
+
+bool CheckLoD(const LoD& in, int tensor_height = -1);
+/*
+ * Check whether this absolute lod's format is valid.
+ *
+ * ATTENTION:
+ *   - Empty lod is treated as valid.
+ *
+ * It will check two things:
+ *  1. all the offsets in a level should be ascending(no same items allows)
+ *  2. there should be more than 2 offsets existing in each level.
+ *  3. the first offset of each level should be 0, and the last should be the
+ *     same(the height of underlying tensor) or `tensor_height` if
+ *     tensor_height>0.
+ */
+bool CheckAbsLoD(const LoD& in, int tensor_height = -1);
+
 /*
  * LoDTensor (Level of details Tensor)
  * see https://en.wikipedia.org/wiki/Level_of_details for reference.

paddle/framework/lod_tensor_test.cc

@@ -37,36 +37,6 @@ namespace framework {
 
 const int kLodTensorSize = 20 * 128;
 
-class LoDTensorTester : public ::testing::Test {
- public:
-  virtual void SetUp() override {
-    // tensor's batch_size: 30
-    // 3 levels
-    // 0 10 20
-    // 0 5 10 15 20
-    // 0 2 5 7 10 12 15 20
-    LoD lod;
-    lod.push_back(std::vector<size_t>{0, 2, 3});
-    lod.push_back(std::vector<size_t>{0, 2, 5, 8});
-    lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
-
-    ASSERT_EQ(lod.size(), 3UL);
-
-    lod_tensor_.Resize({20 /*batch size*/, 128 /*dim*/});
-    // malloc memory
-    float* dst_ptr = lod_tensor_.mutable_data<float>(place);
-    for (int i = 0; i < kLodTensorSize; ++i) {
-      dst_ptr[i] = i;
-    }
-
-    lod_tensor_.set_lod(lod);
-  }
-
- protected:
-  platform::CPUPlace place;
-  LoDTensor lod_tensor_;
-};
-
 TEST(LodExpand, test) {
   LoD lod{{0, 2}};
   LoDTensor tensor;
@@ -144,5 +114,53 @@ TEST(LoD, ToAbsOffset) {
   EXPECT_EQ(abs_lod, expected);
 }
 
+TEST(LoD, CheckLoD) {
+  LoD relative_lod;
+  relative_lod.push_back(std::vector<size_t>({0, 2}));
+  relative_lod.push_back(std::vector<size_t>({0, 1, 3}));
+  relative_lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
+
+  // check compatible
+  ASSERT_TRUE(CheckLoD(relative_lod));
+  relative_lod[1].back()++;
+  ASSERT_FALSE(CheckLoD(relative_lod));
+  relative_lod[1].back()--;  // recover it
+
+  // check empty
+  LoD empty_lod;
+  ASSERT_TRUE(CheckLoD(empty_lod));
+
+  // check less than 2 offsets in a level
+  LoD some_lod0;
+  some_lod0.push_back(std::vector<size_t>({0}));
+  ASSERT_FALSE(CheckLoD(some_lod0));
+
+  // check with underlying tensor storage.
+  ASSERT_TRUE(CheckLoD(relative_lod, 5));
+  ASSERT_FALSE(CheckLoD(relative_lod, 9));
+}
+
+TEST(LoD, CheckAbsLoD) {
+  LoD relative_lod;
+  relative_lod.push_back(std::vector<size_t>({0, 2}));
+  relative_lod.push_back(std::vector<size_t>({0, 1, 3}));
+  relative_lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
+
+  auto abs_lod = ToAbsOffset(relative_lod);
+
+  ASSERT_TRUE(CheckAbsLoD(abs_lod));
+
+  // check less than 2 offsets in a level.
+
+  // check the last item should be compatible with tensor height.
+  abs_lod.back().back()++;
+  ASSERT_FALSE(CheckAbsLoD(abs_lod));
+  abs_lod.back().back()--;  // restore
+
+  // check less than 2 offsets in a lod.
+  LoD abs_lod0;
+  abs_lod0.push_back(std::vector<size_t>({0}));
+  ASSERT_FALSE(CheckAbsLoD(abs_lod0));
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry.h

@@ -177,16 +177,16 @@ class OpKernelRegistrar : public Registrar {
 /**
  * Macro to register OperatorKernel.
  */
-#define REGISTER_OP_KERNEL(op_type, DEVICE_TYPE, place_class, ...)        \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                                         \
-      __reg_op_kernel_##op_type##_##DEVICE_TYPE##__,                      \
-      "REGISTER_OP_KERNEL must be called in global namespace");           \
-  static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__> \
-      __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__(#op_type,       \
-                                                          #DEVICE_TYPE);  \
-  int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE() {                \
-    __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__.Touch();          \
-    return 0;                                                             \
+#define REGISTER_OP_KERNEL(op_type, LIBRARY_TYPE, place_class, ...)        \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                          \
+      __reg_op_kernel_##op_type##_##LIBRARY_TYPE##__,                      \
+      "REGISTER_OP_KERNEL must be called in global namespace");            \
+  static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__>  \
+      __op_kernel_registrar_##op_type##_##LIBRARY_TYPE##__(#op_type,       \
+                                                           #LIBRARY_TYPE); \
+  int TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE() {                \
+    __op_kernel_registrar_##op_type##_##LIBRARY_TYPE##__.Touch();          \
+    return 0;                                                              \
   }
 
 #define REGISTER_OP_CUDA_KERNEL(op_type, ...) \
@@ -208,14 +208,14 @@ class OpKernelRegistrar : public Registrar {
   static int use_op_itself_##op_type##_ __attribute__((unused)) = \
       TouchOpRegistrar_##op_type()
 
-#define USE_OP_DEVICE_KERNEL(op_type, DEVICE_TYPE)               \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(                                \
-      __use_op_kernel_##op_type##_##DEVICE_TYPE##__,             \
-      "USE_OP_DEVICE_KERNEL must be in global namespace");       \
-  extern int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE(); \
-  static int use_op_kernel_##op_type##_##DEVICE_TYPE##_          \
-      __attribute__((unused)) =                                  \
-          TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE()
+#define USE_OP_DEVICE_KERNEL(op_type, LIBRARY_TYPE)               \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                 \
+      __use_op_kernel_##op_type##_##LIBRARY_TYPE##__,             \
+      "USE_OP_DEVICE_KERNEL must be in global namespace");        \
+  extern int TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE(); \
+  static int use_op_kernel_##op_type##_##LIBRARY_TYPE##_          \
+      __attribute__((unused)) =                                   \
+          TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE()
 
 // TODO(fengjiayi): The following macros
 // seems ugly, do we have better method?

paddle/gserver/layers/MKLDNNConcatLayer.cpp

@@ -43,7 +43,7 @@ void MKLDNNConcatLayer::reshape(
   channels_[0] = ic;
   oc = ic;
   for (size_t i = 1; i < inputLayers_.size(); i++) {
-    int batchsize, height, witdh;
+    int batchsize = 0, height = 0, witdh = 0;
     reshapeInput(batchsize, height, witdh, i);
     CHECK_EQ(bs, batchsize);
     CHECK_EQ(ih, height);
@@ -84,6 +84,7 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
   bool has8c = false, has16c = false, hasnc = false;
   for (size_t i = 0; i < inputs.size(); i++) {
     resetInValue(inputs[i], nullptr, i, channels_[i]);
+    inputs[i]->downSpatial();
     CHECK(inputs[i]);
     auto dm = inputs[i]->getDims();
     // inputs format can be different, but ndims must equal

paddle/gserver/tests/img_conv_cudnn.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/img_conv_exconv.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/pyDataProvider.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import numpy
 import struct
 import traceback

paddle/gserver/tests/rnn_data_provider.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 from paddle.trainer.PyDataProvider2 import *
 
 # Note that each config should has an independent provider

paddle/gserver/tests/sequenceGen.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import os
 import sys
 

paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py

-# edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 from paddle.trainer_config_helpers import *
 
 ######################## data source ################################

paddle/gserver/tests/sequence_recurrent.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-#!/usr/bin/env python
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/sequence_recurrent_group.py

-#!/usr/bin/env python
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 from paddle.trainer_config_helpers import *
 
 ######################## data source ################################

paddle/gserver/tests/sequence_rnn_matched_inputs.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-# edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/sequence_rnn_mixed_inputs.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-# edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -11,20 +11,6 @@
 #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #See the License for the specific language governing permissions and
 #limitations under the License.
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 
 from paddle.trainer_config_helpers import *
 

paddle/gserver/tests/test_PyDataProvider2.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import random
 
 from paddle.trainer.PyDataProvider2 import *

python/paddle/v2/fluid/layers/nn.py

@@ -51,6 +51,7 @@ __all__ = [
     'dropout',
     'split',
     'l2_normalize',
+    'matmul',
 ]
 
 
@@ -1697,3 +1698,74 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
                 "Y": rsquare_expanded},
         outputs={"Out": out})
     return out
+
+
+def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
+    """
+    Applies matrix multipication to two tensors. Currently only rank 1 to rank 
+    3 input tensors are supported.
+
+    The actual behavior depends on the shapes of :math:`x`, :math:`y` and the 
+    flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
+
+    - If a transpose flag is specified, the last two dimensions of the tensor 
+      are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for 
+      :math:`x` it is treated as :math:`[1, D]` in nontransposed form and as 
+      :math:`[D, 1]` in transposed form, whereas for :math:`y` it is the 
+      opposite: It is treated as :math:`[D, 1]` in nontransposed form and as 
+      :math:`[1, D]` in transposed form.
+
+    - After transpose, the two tensors are 2-D or 3-D and matrix multipication 
+      performs in the following way.
+
+      - If both are 2-D, they are multiplied like conventional matrices.
+      - If either is 3-D, it is treated as a stack of matrices residing in the 
+        last two dimensions and a batched matrix multiply supporting broadcast 
+        applies on the two tensors.
+
+    Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and 
+    nontransposed, the prepended or appended dimension :math:`1` will be 
+    removed after matrix multipication.
+
+    Args:
+        x (Variable): The input variable which is a Tensor or LoDTensor.
+        y (Variable): The input variable which is a Tensor or LoDTensor.
+        transpose_x (bool): Whether to transpose :math:`x` before multiplication.
+        transpose_y (bool): Whether to transpose :math:`y` before multiplication.
+        name(str|None): A name for this layer(optional). If set None, the layer 
+            will be named automatically.
+
+    Returns:
+        Variable: The product Tensor variable.
+
+    Examples:
+        .. code-block:: python
+
+            # Examples to clarify shapes of the inputs and output
+            # x: [B, M, K], y: [B, K, N]
+            fluid.layers.matmul(x, y)  # out: [B, M, N]
+            # x: [B, M, K], y: [K, N]
+            fluid.layers.matmul(x, y)  # out: [B, M, N]
+            # x: [B, M, K], y: [K]
+            fluid.layers.matmul(x, y)  # out: [B, M]
+            # x: [M, K], y: [K, N]
+            fluid.layers.matmul(x, y)  # out: [M, N]
+            # x: [K], y: [K]
+            fluid.layers.matmul(x, y)  # out: [1]
+            # x: [M], y: [N]
+
+            fluid.layers.matmul(x, y, True, True)  # out: [M, N]
+    """
+    helper = LayerHelper('matmul', **locals())
+    assert max(
+        len(x.shape), len(y.shape)
+    ) <= 3, 'Currently only rank 1 to rank 3 input tensors are supported.'
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='matmul',
+        inputs={'X': x,
+                'Y': y},
+        outputs={'Out': out},
+        attrs={'transpose_X': transpose_x,
+               'transpose_Y': transpose_y})
+    return out

python/paddle/v2/fluid/nets.py

@@ -17,6 +17,7 @@ __all__ = [
     "simple_img_conv_pool",
     "sequence_conv_pool",
     "glu",
+    "dot_product_attention",
 ]
 
 
@@ -150,3 +151,55 @@ def glu(input, dim=-1):
     act_b = layers.sigmoid(x=b)
     out = layers.elementwise_mul(x=a, y=act_b)
     return out
+
+
+def dot_product_attention(querys, keys, values):
+    """
+    The dot-product attention.
+
+    Attention mechanism can be seen as mapping a query and a set of key-value 
+    pairs to an output. The output is computed as a weighted sum of the values, 
+    where the weight assigned to each value is computed by a compatibility 
+    function (dot-product here) of the query with the corresponding key.
+    
+    The dot-product attention can be implemented through (batch) matrix 
+    multipication as follows:
+
+        .. math::
+
+            Attention(Q, K, V)= softmax(QK^\mathrm{T})V
+
+    Refer to `Attention Is All You Need 
+    <https://arxiv.org/pdf/1706.03762.pdf>`_.
+
+    Note that batch data containing sequences with different lengths is not 
+    supported by this because of the (batch) matrix multipication.
+    
+    Args:
+        query (Variable): The input variable which is a Tensor or LoDTensor.
+        key (Variable): The input variable which is a Tensor or LoDTensor.
+        value (Variable): The input variable which is a Tensor or LoDTensor.
+
+    Returns:
+        tuple: The Tensor variables representing the output and attention scores.
+
+    Examples:
+        .. code-block:: python
+
+            # Suppose q, k, v are tensor variables with the following shape:
+            # q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
+            out, attn_scores = fluid.nets.dot_product_attention(q, k, v)
+            out.shape  # [3, 5, 10]
+            attn_scores.shape  # [3, 5, 6]
+    """
+    assert keys.shape[-2] == values.shape[
+        -2], 'The shapes of keys and values mismatch.'
+    assert querys.shape[-1] == keys.shape[
+        -1], 'The shapes of querys and keys mismatch.'
+    product = layers.matmul(x=querys, y=keys, transpose_y=True)
+    attn_scores = layers.reshape(
+        x=layers.reshape(
+            x=product, shape=[-1, product.shape[-1]], act='softmax'),
+        shape=product.shape)
+    out = layers.matmul(attn_scores, values)
+    return out, attn_scores

python/paddle/v2/fluid/tests/test_matmul_op.py

@@ -96,18 +96,18 @@ class Generator(object):
         self.outputs = {'Out': Out}
 
     def test_check_output(self):
-        self.check_output(atol=1e-2)
+        self.check_output(atol=1e-3)
 
     def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=1e-3)
 
     def test_check_grad_ignore_x(self):
         self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
+            ['Y'], 'Out', max_relative_error=1e-3, no_grad_set=set("X"))
 
     def test_check_grad_ignore_y(self):
         self.check_grad(
-            ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
+            ['X'], 'Out', max_relative_error=1e-3, no_grad_set=set('Y'))
 
 
 # Generate test cases for all possibilities

v1_api_demo/README.md

-The examples in v1_api_demo are using v1_api currently, and will be upgraded to v2_api later.
-Thus, v1_api_demo is a temporary directory. We decide not to maintain it and will delete it in future.
-
-Please go to [PaddlePaddle/book](https://github.com/PaddlePaddle/book) and 
-[PaddlePaddle/models](https://github.com/PaddlePaddle/models) to learn PaddlePaddle.

v1_api_demo/gan/.gitignore

-output/
-uniform_params/
-cifar_params/
-mnist_params/
-*.png
-.pydevproject
-.project
-*.log
-*.pyc
-data/mnist_data/
-data/cifar-10-batches-py/

v1_api_demo/gan/README.md

-# Generative Adversarial Networks (GAN) 
-
-This demo implements GAN training described in the original GAN paper (https://arxiv.org/abs/1406.2661) and DCGAN (https://arxiv.org/abs/1511.06434).
-
-The general training procedures are implemented in gan_trainer.py. The neural network configurations are specified in gan_conf.py (for synthetic data) and gan_conf_image.py (for image data).
-
-In order to run the model, first download the corresponding data by running the shell script in ./data.
-Then you can run the command below. The flag -d specifies the training data (cifar, mnist or uniform) and flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).  
-
-$python gan_trainer.py -d cifar --use_gpu 1
-
-The generated images will be stored in ./cifar_samples/
-The corresponding models will be stored in ./cifar_params/

v1_api_demo/gan/data/download_cifar.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
-tar zxf cifar-10-python.tar.gz
-rm cifar-10-python.tar.gz

v1_api_demo/gan/data/get_mnist_data.sh

-#!/usr/bin/env sh
-# This script downloads the mnist data and unzips it.
-set -e
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-rm -rf "$DIR/mnist_data"
-mkdir "$DIR/mnist_data"
-cd "$DIR/mnist_data"
-
-echo "Downloading..."
-
-for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
-do
-    if [ ! -e $fname ]; then
-        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
-        gunzip ${fname}.gz
-    fi
-done

v1_api_demo/gan/gan_conf.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from paddle.trainer_config_helpers import *
-
-mode = get_config_arg("mode", str, "generator")
-assert mode in set([
-    "generator", "discriminator", "generator_training", "discriminator_training"
-])
-
-is_generator_training = mode == "generator_training"
-is_discriminator_training = mode == "discriminator_training"
-is_generator = mode == "generator"
-is_discriminator = mode == "discriminator"
-
-# The network structure below follows the ref https://arxiv.org/abs/1406.2661
-# Here we used two hidden layers and batch_norm
-
-print('mode=%s' % mode)
-# the dim of the noise (z) as the input of the generator network
-noise_dim = 10
-# the dim of the hidden layer
-hidden_dim = 10
-# the dim of the generated sample
-sample_dim = 2
-
-settings(
-    batch_size=128,
-    learning_rate=1e-4,
-    learning_method=AdamOptimizer(beta1=0.5))
-
-
-def discriminator(sample):
-    """
-    discriminator ouputs the probablity of a sample is from generator
-    or real data.
-    The output has two dimenstional: dimension 0 is the probablity
-    of the sample is from generator and dimension 1 is the probabblity
-    of the sample is from real data.
-    """
-    param_attr = ParamAttr(is_static=is_generator_training)
-    bias_attr = ParamAttr(
-        is_static=is_generator_training, initial_mean=1.0, initial_std=0)
-
-    hidden = fc_layer(
-        input=sample,
-        name="dis_hidden",
-        size=hidden_dim,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=ReluActivation())
-
-    hidden2 = fc_layer(
-        input=hidden,
-        name="dis_hidden2",
-        size=hidden_dim,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=LinearActivation())
-
-    hidden_bn = batch_norm_layer(
-        hidden2,
-        act=ReluActivation(),
-        name="dis_hidden_bn",
-        bias_attr=bias_attr,
-        param_attr=ParamAttr(
-            is_static=is_generator_training, initial_mean=1.0,
-            initial_std=0.02),
-        use_global_stats=False)
-
-    return fc_layer(
-        input=hidden_bn,
-        name="dis_prob",
-        size=2,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=SoftmaxActivation())
-
-
-def generator(noise):
-    """
-    generator generates a sample given noise
-    """
-    param_attr = ParamAttr(is_static=is_discriminator_training)
-    bias_attr = ParamAttr(
-        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
-
-    hidden = fc_layer(
-        input=noise,
-        name="gen_layer_hidden",
-        size=hidden_dim,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=ReluActivation())
-
-    hidden2 = fc_layer(
-        input=hidden,
-        name="gen_hidden2",
-        size=hidden_dim,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=LinearActivation())
-
-    hidden_bn = batch_norm_layer(
-        hidden2,
-        act=ReluActivation(),
-        name="gen_layer_hidden_bn",
-        bias_attr=bias_attr,
-        param_attr=ParamAttr(
-            is_static=is_discriminator_training,
-            initial_mean=1.0,
-            initial_std=0.02),
-        use_global_stats=False)
-
-    return fc_layer(
-        input=hidden_bn,
-        name="gen_layer1",
-        size=sample_dim,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=LinearActivation())
-
-
-if is_generator_training:
-    noise = data_layer(name="noise", size=noise_dim)
-    sample = generator(noise)
-
-if is_discriminator_training:
-    sample = data_layer(name="sample", size=sample_dim)
-
-if is_generator_training or is_discriminator_training:
-    label = data_layer(name="label", size=1)
-    prob = discriminator(sample)
-    cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(
-        input=prob, label=label, name=mode + '_error')
-    outputs(cost)
-
-if is_generator:
-    noise = data_layer(name="noise", size=noise_dim)
-    outputs(generator(noise))

v1_api_demo/gan/gan_conf_image.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from paddle.trainer_config_helpers import *
-
-mode = get_config_arg("mode", str, "generator")
-dataSource = get_config_arg("data", str, "mnist")
-assert mode in set([
-    "generator", "discriminator", "generator_training", "discriminator_training"
-])
-
-is_generator_training = mode == "generator_training"
-is_discriminator_training = mode == "discriminator_training"
-is_generator = mode == "generator"
-is_discriminator = mode == "discriminator"
-
-# The network structure below follows the dcgan paper 
-# (https://arxiv.org/abs/1511.06434)
-
-print('mode=%s' % mode)
-# the dim of the noise (z) as the input of the generator network
-noise_dim = 100
-# the number of filters in the layer in generator/discriminator that is 
-# closet to the image
-gf_dim = 64
-df_dim = 64
-if dataSource == "mnist":
-    sample_dim = 28  # image dim
-    c_dim = 1  # image color
-else:
-    sample_dim = 32
-    c_dim = 3
-s2, s4 = int(sample_dim / 2), int(sample_dim / 4),
-s8, s16 = int(sample_dim / 8), int(sample_dim / 16)
-
-settings(
-    batch_size=128,
-    learning_rate=2e-4,
-    learning_method=AdamOptimizer(beta1=0.5))
-
-
-def conv_bn(input,
-            channels,
-            imgSize,
-            num_filters,
-            output_x,
-            stride,
-            name,
-            param_attr,
-            bias_attr,
-            param_attr_bn,
-            bn,
-            trans=False,
-            act=ReluActivation()):
-    """
-    conv_bn is a utility function that constructs a convolution/deconv layer 
-    with an optional batch_norm layer
-
-    :param bn: whether to use batch_norm_layer
-    :type bn: bool
-    :param trans: whether to use conv (False) or deconv (True)
-    :type trans: bool
-    """
-
-    # calculate the filter_size and padding size based on the given
-    # imgSize and ouput size
-    tmp = imgSize - (output_x - 1) * stride
-    if tmp <= 1 or tmp > 5:
-        raise ValueError("conv input-output dimension does not fit")
-    elif tmp <= 3:
-        filter_size = tmp + 2
-        padding = 1
-    else:
-        filter_size = tmp
-        padding = 0
-
-    print(imgSize, output_x, stride, filter_size, padding)
-
-    if trans:
-        nameApx = "_convt"
-    else:
-        nameApx = "_conv"
-
-    if bn:
-        conv = img_conv_layer(
-            input,
-            filter_size=filter_size,
-            num_filters=num_filters,
-            name=name + nameApx,
-            num_channels=channels,
-            act=LinearActivation(),
-            groups=1,
-            stride=stride,
-            padding=padding,
-            bias_attr=bias_attr,
-            param_attr=param_attr,
-            shared_biases=True,
-            layer_attr=None,
-            filter_size_y=None,
-            stride_y=None,
-            padding_y=None,
-            trans=trans)
-
-        conv_bn = batch_norm_layer(
-            conv,
-            act=act,
-            name=name + nameApx + "_bn",
-            bias_attr=bias_attr,
-            param_attr=param_attr_bn,
-            use_global_stats=False)
-
-        return conv_bn
-    else:
-        conv = img_conv_layer(
-            input,
-            filter_size=filter_size,
-            num_filters=num_filters,
-            name=name + nameApx,
-            num_channels=channels,
-            act=act,
-            groups=1,
-            stride=stride,
-            padding=padding,
-            bias_attr=bias_attr,
-            param_attr=param_attr,
-            shared_biases=True,
-            layer_attr=None,
-            filter_size_y=None,
-            stride_y=None,
-            padding_y=None,
-            trans=trans)
-        return conv
-
-
-def generator(noise):
-    """
-    generator generates a sample given noise
-    """
-    param_attr = ParamAttr(
-        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02)
-    bias_attr = ParamAttr(
-        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0)
-
-    param_attr_bn = ParamAttr(
-        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02)
-
-    h1 = fc_layer(
-        input=noise,
-        name="gen_layer_h1",
-        size=s8 * s8 * gf_dim * 4,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=LinearActivation())
-
-    h1_bn = batch_norm_layer(
-        h1,
-        act=ReluActivation(),
-        name="gen_layer_h1_bn",
-        bias_attr=bias_attr,
-        param_attr=param_attr_bn,
-        use_global_stats=False)
-
-    h2_bn = conv_bn(
-        h1_bn,
-        channels=gf_dim * 4,
-        output_x=s8,
-        num_filters=gf_dim * 2,
-        imgSize=s4,
-        stride=2,
-        name="gen_layer_h2",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=True,
-        trans=True)
-
-    h3_bn = conv_bn(
-        h2_bn,
-        channels=gf_dim * 2,
-        output_x=s4,
-        num_filters=gf_dim,
-        imgSize=s2,
-        stride=2,
-        name="gen_layer_h3",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=True,
-        trans=True)
-
-    return conv_bn(
-        h3_bn,
-        channels=gf_dim,
-        output_x=s2,
-        num_filters=c_dim,
-        imgSize=sample_dim,
-        stride=2,
-        name="gen_layer_h4",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=False,
-        trans=True,
-        act=TanhActivation())
-
-
-def discriminator(sample):
-    """
-    discriminator ouputs the probablity of a sample is from generator
-    or real data.
-    The output has two dimenstional: dimension 0 is the probablity
-    of the sample is from generator and dimension 1 is the probabblity
-    of the sample is from real data.
-    """
-    param_attr = ParamAttr(
-        is_static=is_generator_training, initial_mean=0.0, initial_std=0.02)
-    bias_attr = ParamAttr(
-        is_static=is_generator_training, initial_mean=0.0, initial_std=0.0)
-
-    param_attr_bn = ParamAttr(
-        is_static=is_generator_training, initial_mean=1.0, initial_std=0.02)
-
-    h0 = conv_bn(
-        sample,
-        channels=c_dim,
-        imgSize=sample_dim,
-        num_filters=df_dim,
-        output_x=s2,
-        stride=2,
-        name="dis_h0",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=False)
-
-    h1_bn = conv_bn(
-        h0,
-        channels=df_dim,
-        imgSize=s2,
-        num_filters=df_dim * 2,
-        output_x=s4,
-        stride=2,
-        name="dis_h1",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=True)
-
-    h2_bn = conv_bn(
-        h1_bn,
-        channels=df_dim * 2,
-        imgSize=s4,
-        num_filters=df_dim * 4,
-        output_x=s8,
-        stride=2,
-        name="dis_h2",
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        param_attr_bn=param_attr_bn,
-        bn=True)
-
-    return fc_layer(
-        input=h2_bn,
-        name="dis_prob",
-        size=2,
-        bias_attr=bias_attr,
-        param_attr=param_attr,
-        act=SoftmaxActivation())
-
-
-if is_generator_training:
-    noise = data_layer(name="noise", size=noise_dim)
-    sample = generator(noise)
-
-if is_discriminator_training:
-    sample = data_layer(name="sample", size=sample_dim * sample_dim * c_dim)
-
-if is_generator_training or is_discriminator_training:
-    label = data_layer(name="label", size=1)
-    prob = discriminator(sample)
-    cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(
-        input=prob, label=label, name=mode + '_error')
-    outputs(cost)
-
-if is_generator:
-    noise = data_layer(name="noise", size=noise_dim)
-    outputs(generator(noise))

v1_api_demo/gan/gan_trainer.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import random
-import numpy
-import cPickle
-import sys, os
-from PIL import Image
-
-from paddle.trainer.config_parser import parse_config
-from paddle.trainer.config_parser import logger
-import py_paddle.swig_paddle as api
-import matplotlib.pyplot as plt
-
-
-def plot2DScatter(data, outputfile):
-    '''
-    Plot the data as a 2D scatter plot and save to outputfile
-    data needs to be two dimensinoal
-    '''
-    x = data[:, 0]
-    y = data[:, 1]
-    logger.info("The mean vector is %s" % numpy.mean(data, 0))
-    logger.info("The std vector is %s" % numpy.std(data, 0))
-
-    heatmap, xedges, yedges = numpy.histogram2d(x, y, bins=50)
-    extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
-
-    plt.clf()
-    plt.scatter(x, y)
-    plt.savefig(outputfile, bbox_inches='tight')
-
-
-def CHECK_EQ(a, b):
-    assert a == b, "a=%s, b=%s" % (a, b)
-
-
-def copy_shared_parameters(src, dst):
-    '''
-    copy the parameters from src to dst
-    :param src: the source of the parameters
-    :type src: GradientMachine
-    :param dst: the destination of the parameters
-    :type dst: GradientMachine
-    '''
-    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
-    src_params = dict([(p.getName(), p) for p in src_params])
-
-    for i in xrange(dst.getParameterSize()):
-        dst_param = dst.getParameter(i)
-        src_param = src_params.get(dst_param.getName(), None)
-        if src_param is None:
-            continue
-        src_value = src_param.getBuf(api.PARAMETER_VALUE)
-        dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
-        CHECK_EQ(len(src_value), len(dst_value))
-        dst_value.copyFrom(src_value)
-        dst_param.setValueUpdated()
-
-
-def print_parameters(src):
-    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
-
-    print "***************"
-    for p in src_params:
-        print "Name is %s" % p.getName()
-        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
-        )
-
-
-def load_mnist_data(imageFile):
-    f = open(imageFile, "rb")
-    f.read(16)
-
-    # Define number of samples for train/test
-    if "train" in imageFile:
-        n = 60000
-    else:
-        n = 10000
-
-    data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
-    data = data / 255.0 * 2.0 - 1.0
-
-    f.close()
-    return data.astype('float32')
-
-
-def load_cifar_data(cifar_path):
-    batch_size = 10000
-    data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
-    for i in range(1, 6):
-        file = cifar_path + "/data_batch_" + str(i)
-        fo = open(file, 'rb')
-        dict = cPickle.load(fo)
-        fo.close()
-        data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
-
-    data = data / 255.0 * 2.0 - 1.0
-    return data
-
-
-# synthesize 2-D uniform data
-def load_uniform_data():
-    data = numpy.random.rand(1000000, 2).astype('float32')
-    return data
-
-
-def merge(images, size):
-    if images.shape[1] == 28 * 28:
-        h, w, c = 28, 28, 1
-    else:
-        h, w, c = 32, 32, 3
-    img = numpy.zeros((h * size[0], w * size[1], c))
-    for idx in xrange(size[0] * size[1]):
-        i = idx % size[1]
-        j = idx // size[1]
-        img[j*h:j*h+h, i*w:i*w+w, :] = \
-          ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
-    return img.astype('uint8')
-
-
-def save_images(images, path):
-    merged_img = merge(images, [8, 8])
-    if merged_img.shape[2] == 1:
-        im = Image.fromarray(numpy.squeeze(merged_img)).convert('RGB')
-    else:
-        im = Image.fromarray(merged_img, mode="RGB")
-    im.save(path)
-
-
-def get_real_samples(batch_size, data_np):
-    return data_np[numpy.random.choice(
-        data_np.shape[0], batch_size, replace=False), :]
-
-
-def get_noise(batch_size, noise_dim):
-    return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
-
-
-def get_fake_samples(generator_machine, batch_size, noise):
-    gen_inputs = api.Arguments.createArguments(1)
-    gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
-    gen_outputs = api.Arguments.createArguments(0)
-    generator_machine.forward(gen_inputs, gen_outputs, api.PASS_TEST)
-    fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
-    return fake_samples
-
-
-def get_training_loss(training_machine, inputs):
-    outputs = api.Arguments.createArguments(0)
-    training_machine.forward(inputs, outputs, api.PASS_TEST)
-    loss = outputs.getSlotValue(0).copyToNumpyMat()
-    return numpy.mean(loss)
-
-
-def prepare_discriminator_data_batch_pos(batch_size, data_np):
-    real_samples = get_real_samples(batch_size, data_np)
-    labels = numpy.ones(batch_size, dtype='int32')
-    inputs = api.Arguments.createArguments(2)
-    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(real_samples))
-    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
-    return inputs
-
-
-def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
-    fake_samples = get_fake_samples(generator_machine, batch_size, noise)
-    labels = numpy.zeros(batch_size, dtype='int32')
-    inputs = api.Arguments.createArguments(2)
-    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(fake_samples))
-    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
-    return inputs
-
-
-def prepare_generator_data_batch(batch_size, noise):
-    label = numpy.ones(batch_size, dtype='int32')
-    inputs = api.Arguments.createArguments(2)
-    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
-    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(label))
-    return inputs
-
-
-def find(iterable, cond):
-    for item in iterable:
-        if cond(item):
-            return item
-    return None
-
-
-def get_layer_size(model_conf, layer_name):
-    layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
-    assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
-    return layer_conf.size
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
-    parser.add_argument(
-        "--use_gpu", default="1", help="1 means use gpu for training")
-    parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
-    args = parser.parse_args()
-    data_source = args.data_source
-    use_gpu = args.use_gpu
-    assert data_source in ["mnist", "cifar", "uniform"]
-    assert use_gpu in ["0", "1"]
-
-    if not os.path.exists("./%s_samples/" % data_source):
-        os.makedirs("./%s_samples/" % data_source)
-
-    if not os.path.exists("./%s_params/" % data_source):
-        os.makedirs("./%s_params/" % data_source)
-
-    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
-                   '--log_period=100', '--gpu_id=' + args.gpu_id,
-                   '--save_dir=' + "./%s_params/" % data_source)
-
-    if data_source == "uniform":
-        conf = "gan_conf.py"
-        num_iter = 10000
-    else:
-        conf = "gan_conf_image.py"
-        num_iter = 1000
-
-    gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
-    dis_conf = parse_config(conf,
-                            "mode=discriminator_training,data=" + data_source)
-    generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
-    batch_size = dis_conf.opt_config.batch_size
-    noise_dim = get_layer_size(gen_conf.model_config, "noise")
-
-    if data_source == "mnist":
-        data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
-    elif data_source == "cifar":
-        data_np = load_cifar_data("./data/cifar-10-batches-py/")
-    else:
-        data_np = load_uniform_data()
-
-    # this creates a gradient machine for discriminator
-    dis_training_machine = api.GradientMachine.createFromConfigProto(
-        dis_conf.model_config)
-    # this create a gradient machine for generator    
-    gen_training_machine = api.GradientMachine.createFromConfigProto(
-        gen_conf.model_config)
-
-    # generator_machine is used to generate data only, which is used for
-    # training discriminator
-    logger.info(str(generator_conf.model_config))
-    generator_machine = api.GradientMachine.createFromConfigProto(
-        generator_conf.model_config)
-
-    dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
-
-    gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
-
-    dis_trainer.startTrain()
-    gen_trainer.startTrain()
-
-    # Sync parameters between networks (GradientMachine) at the beginning
-    copy_shared_parameters(gen_training_machine, dis_training_machine)
-    copy_shared_parameters(gen_training_machine, generator_machine)
-
-    # constrain that either discriminator or generator can not be trained
-    # consecutively more than MAX_strike times
-    curr_train = "dis"
-    curr_strike = 0
-    MAX_strike = 5
-
-    for train_pass in xrange(100):
-        dis_trainer.startTrainPass()
-        gen_trainer.startTrainPass()
-        for i in xrange(num_iter):
-            # Do forward pass in discriminator to get the dis_loss
-            noise = get_noise(batch_size, noise_dim)
-            data_batch_dis_pos = prepare_discriminator_data_batch_pos(
-                batch_size, data_np)
-            dis_loss_pos = get_training_loss(dis_training_machine,
-                                             data_batch_dis_pos)
-
-            data_batch_dis_neg = prepare_discriminator_data_batch_neg(
-                generator_machine, batch_size, noise)
-            dis_loss_neg = get_training_loss(dis_training_machine,
-                                             data_batch_dis_neg)
-
-            dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
-
-            # Do forward pass in generator to get the gen_loss
-            data_batch_gen = prepare_generator_data_batch(batch_size, noise)
-            gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
-
-            if i % 100 == 0:
-                print "d_pos_loss is %s     d_neg_loss is %s" % (dis_loss_pos,
-                                                                 dis_loss_neg)
-                print "d_loss is %s    g_loss is %s" % (dis_loss, gen_loss)
-
-            # Decide which network to train based on the training history
-            # And the relative size of the loss        
-            if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
-               ((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss > gen_loss):
-                if curr_train == "dis":
-                    curr_strike += 1
-                else:
-                    curr_train = "dis"
-                    curr_strike = 1
-                dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
-                dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
-                copy_shared_parameters(dis_training_machine,
-                                       gen_training_machine)
-
-            else:
-                if curr_train == "gen":
-                    curr_strike += 1
-                else:
-                    curr_train = "gen"
-                    curr_strike = 1
-                gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
-                # TODO: add API for paddle to allow true parameter sharing between different GradientMachines 
-                # so that we do not need to copy shared parameters. 
-                copy_shared_parameters(gen_training_machine,
-                                       dis_training_machine)
-                copy_shared_parameters(gen_training_machine, generator_machine)
-
-        dis_trainer.finishTrainPass()
-        gen_trainer.finishTrainPass()
-        # At the end of each pass, save the generated samples/images
-        fake_samples = get_fake_samples(generator_machine, batch_size, noise)
-        if data_source == "uniform":
-            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
-                          (data_source, train_pass))
-        else:
-            save_images(fake_samples, "./%s_samples/train_pass%s.png" %
-                        (data_source, train_pass))
-    dis_trainer.finishTrain()
-    gen_trainer.finishTrain()
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/mnist/.gitignore

-data/raw_data
-data/*.list
-mnist_vgg_model
-plot.png
-train.log
-*pyc
-.ipynb_checkpoints
-params.pkl
-params.tar
-params.tar.gz

v1_api_demo/mnist/api_train.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-"""
-A very basic example for how to use current Raw SWIG API to train mnist network.
-
-Current implementation uses Raw SWIG, which means the API call is directly \
-passed to C++ side of Paddle.
-
-The user api could be simpler and carefully designed.
-"""
-import random
-
-import numpy as np
-import paddle.v2 as paddle_v2
-import py_paddle.swig_paddle as api
-from paddle.trainer_config_helpers import *
-from py_paddle import DataProviderConverter
-
-from mnist_util import read_from_mnist
-
-
-def init_parameter(network):
-    assert isinstance(network, api.GradientMachine)
-    for each_param in network.getParameters():
-        assert isinstance(each_param, api.Parameter)
-        array_size = len(each_param)
-        array = np.random.uniform(-1.0, 1.0, array_size).astype('float32')
-        each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(array)
-
-
-def generator_to_batch(generator, batch_size):
-    ret_val = list()
-    for each_item in generator:
-        ret_val.append(each_item)
-        if len(ret_val) == batch_size:
-            yield ret_val
-            ret_val = list()
-    if len(ret_val) != 0:
-        yield ret_val
-
-
-class BatchPool(object):
-    def __init__(self, generator, batch_size):
-        self.data = list(generator)
-        self.batch_size = batch_size
-
-    def __call__(self):
-        random.shuffle(self.data)
-        for offset in xrange(0, len(self.data), self.batch_size):
-            limit = min(offset + self.batch_size, len(self.data))
-            yield self.data[offset:limit]
-
-
-def input_order_converter(generator):
-    for each_item in generator:
-        yield each_item['pixel'], each_item['label']
-
-
-def main():
-    api.initPaddle("-use_gpu=false", "-trainer_count=4")  # use 4 cpu cores
-
-    optimizer = paddle_v2.optimizer.Adam(
-        learning_rate=1e-4,
-        batch_size=1000,
-        model_average=ModelAverage(average_window=0.5),
-        regularization=L2Regularization(rate=0.5))
-
-    # Create Local Updater. Local means not run in cluster.
-    # For a cluster training, here we can change to createRemoteUpdater
-    # in future.
-    updater = optimizer.create_local_updater()
-    assert isinstance(updater, api.ParameterUpdater)
-
-    # define network
-    images = paddle_v2.layer.data(
-        name='pixel', type=paddle_v2.data_type.dense_vector(784))
-    label = paddle_v2.layer.data(
-        name='label', type=paddle_v2.data_type.integer_value(10))
-    hidden1 = paddle_v2.layer.fc(input=images, size=200)
-    hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
-    inference = paddle_v2.layer.fc(input=hidden2,
-                                   size=10,
-                                   act=paddle_v2.activation.Softmax())
-    cost = paddle_v2.layer.classification_cost(input=inference, label=label)
-
-    # Create Simple Gradient Machine.
-    model_config = paddle_v2.layer.parse_network(cost)
-    m = api.GradientMachine.createFromConfigProto(model_config,
-                                                  api.CREATE_MODE_NORMAL,
-                                                  optimizer.enable_types())
-
-    # This type check is not useful. Only enable type hint in IDE.
-    # Such as PyCharm
-    assert isinstance(m, api.GradientMachine)
-
-    # Initialize Parameter by numpy.
-    init_parameter(network=m)
-
-    # Initialize ParameterUpdater.
-    updater.init(m)
-
-    # DataProvider Converter is a utility convert Python Object to Paddle C++
-    # Input. The input format is as same as Paddle's DataProvider.
-    converter = DataProviderConverter(input_types=[images.type, label.type])
-
-    train_file = './data/raw_data/train'
-    test_file = './data/raw_data/t10k'
-
-    # start gradient machine.
-    # the gradient machine must be started before invoke forward/backward.
-    # not just for training, but also for inference.
-    m.start()
-
-    # evaluator can print error rate, etc. It is a C++ class.
-    batch_evaluator = m.makeEvaluator()
-    test_evaluator = m.makeEvaluator()
-
-    # Get Train Data.
-    # TrainData will stored in a data pool. Currently implementation is not care
-    # about memory, speed. Just a very naive implementation.
-    train_data_generator = input_order_converter(read_from_mnist(train_file))
-    train_data = BatchPool(train_data_generator, 512)
-
-    # outArgs is Neural Network forward result. Here is not useful, just passed
-    # to gradient_machine.forward
-    outArgs = api.Arguments.createArguments(0)
-
-    for pass_id in xrange(2):  # we train 2 passes.
-        updater.startPass()
-
-        for batch_id, data_batch in enumerate(train_data()):
-            # data_batch is input images.
-            # here, for online learning, we could get data_batch from network.
-
-            # Start update one batch.
-            pass_type = updater.startBatch(len(data_batch))
-
-            # Start BatchEvaluator.
-            # batch_evaluator can be used between start/finish.
-            batch_evaluator.start()
-
-            # forwardBackward is a shortcut for forward and backward.
-            # It is sometimes faster than invoke forward/backward separately,
-            # because in GradientMachine, it may be async.
-            m.forwardBackward(converter(data_batch), outArgs, pass_type)
-
-            for each_param in m.getParameters():
-                updater.update(each_param)
-
-            # Get cost. We use numpy to calculate total cost for this batch.
-            cost_vec = outArgs.getSlotValue(0)
-            cost_vec = cost_vec.copyToNumpyMat()
-            cost = cost_vec.sum() / len(data_batch)
-
-            # Make evaluator works.
-            m.eval(batch_evaluator)
-
-            # Print logs.
-            print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
-                cost, batch_evaluator
-
-            batch_evaluator.finish()
-            # Finish batch.
-            #  * will clear gradient.
-            #  * ensure all values should be updated.
-            updater.finishBatch(cost)
-
-        # testing stage. use test data set to test current network.
-        updater.apply()
-        test_evaluator.start()
-        test_data_generator = input_order_converter(read_from_mnist(test_file))
-        for data_batch in generator_to_batch(test_data_generator, 512):
-            # in testing stage, only forward is needed.
-            m.forward(converter(data_batch), outArgs, api.PASS_TEST)
-            m.eval(test_evaluator)
-
-        # print error rate for test data set
-        print 'Pass', pass_id, ' test evaluator: ', test_evaluator
-        test_evaluator.finish()
-        updater.restore()
-
-        updater.catchUpWith()
-        params = m.getParameters()
-        for each_param in params:
-            assert isinstance(each_param, api.Parameter)
-            value = each_param.getBuf(api.PARAMETER_VALUE)
-            value = value.copyToNumpyArray()
-
-            # Here, we could save parameter to every where you want
-            print each_param.getName(), value
-
-        updater.finishPass()
-
-    m.finish()
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/mnist/data/generate_list.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-o = open("./" + "train.list", "w")
-o.write("./data/raw_data/train" + "\n")
-o.close()
-
-o = open("./" + "test.list", "w")
-o.write("./data/raw_data/t10k" + "\n")
-o.close()

v1_api_demo/mnist/data/get_mnist_data.sh

-#!/usr/bin/env sh
-# This scripts downloads the mnist data and unzips it.
-set -e
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-rm -rf "$DIR/raw_data"
-mkdir "$DIR/raw_data"
-cd "$DIR/raw_data"
-
-echo "Downloading..."
-
-for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
-do
-    if [ ! -e $fname ]; then
-        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
-        gunzip ${fname}.gz
-    fi
-done
-
-cd $DIR
-rm -f *.list
-python generate_list.py

v1_api_demo/mnist/light_mnist.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-is_predict = get_config_arg("is_predict", bool, False)
-
-####################Data Configuration ##################
-
-if not is_predict:
-    data_dir = './data/'
-    define_py_data_sources2(
-        train_list=data_dir + 'train.list',
-        test_list=data_dir + 'test.list',
-        module='mnist_provider',
-        obj='process')
-
-######################Algorithm Configuration #############
-settings(batch_size=50, learning_rate=0.001, learning_method=AdamOptimizer())
-
-#######################Network Configuration #############
-
-data_size = 1 * 28 * 28
-label_size = 10
-img = data_layer(name='pixel', size=data_size)
-
-
-# light cnn
-# A shallower cnn model: [CNN, BN, ReLU, Max-Pooling] x4 + FC x1
-# Easier to train for mnist dataset and quite efficient
-# Final performance is close to deeper ones on tasks such as digital and character classification 
-def light_cnn(input_image, num_channels, num_classes):
-    def __light__(ipt,
-                  num_filter=128,
-                  times=1,
-                  conv_filter_size=3,
-                  dropouts=0,
-                  num_channels_=None):
-        return img_conv_group(
-            input=ipt,
-            num_channels=num_channels_,
-            pool_size=2,
-            pool_stride=2,
-            conv_padding=0,
-            conv_num_filter=[num_filter] * times,
-            conv_filter_size=conv_filter_size,
-            conv_act=ReluActivation(),
-            conv_with_batchnorm=True,
-            conv_batchnorm_drop_rate=dropouts,
-            pool_type=MaxPooling())
-
-    tmp = __light__(input_image, num_filter=128, num_channels_=num_channels)
-    tmp = __light__(tmp, num_filter=128)
-    tmp = __light__(tmp, num_filter=128)
-    tmp = __light__(tmp, num_filter=128, conv_filter_size=1)
-
-    tmp = fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
-    return tmp
-
-
-predict = light_cnn(input_image=img, num_channels=1, num_classes=label_size)
-
-if not is_predict:
-    lbl = data_layer(name="label", size=label_size)
-    inputs(img, lbl)
-    outputs(classification_cost(input=predict, label=lbl))
-else:
-    outputs(predict)

v1_api_demo/mnist/mnist_provider.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-from paddle.trainer.PyDataProvider2 import *
-from mnist_util import read_from_mnist
-
-
-# Define a py data provider
-@provider(
-    input_types={'pixel': dense_vector(28 * 28),
-                 'label': integer_value(10)},
-    cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, filename):  # settings is not used currently.
-    for each in read_from_mnist(filename):
-        yield each

v1_api_demo/mnist/mnist_util.py

-import numpy
-
-__all__ = ['read_from_mnist']
-
-
-def read_from_mnist(filename):
-    imgf = filename + "-images-idx3-ubyte"
-    labelf = filename + "-labels-idx1-ubyte"
-    f = open(imgf, "rb")
-    l = open(labelf, "rb")
-
-    f.read(16)
-    l.read(8)
-
-    # Define number of samples for train/test
-    if "train" in filename:
-        n = 60000
-    else:
-        n = 10000
-
-    images = numpy.fromfile(
-        f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
-    images = images / 255.0 * 2.0 - 1.0
-    labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
-
-    for i in xrange(n):
-        yield {"pixel": images[i, :], 'label': labels[i]}
-
-    f.close()
-    l.close()

v1_api_demo/mnist/train.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-config=vgg_16_mnist.py
-output=./mnist_vgg_model
-log=train.log
-
-paddle train \
---config=$config \
---dot_period=10 \
---log_period=100 \
---test_all_data_in_one_period=1 \
---use_gpu=0 \
---trainer_count=1 \
---num_passes=100 \
---save_dir=$output \
-2>&1 | tee $log
-paddle usage -l $log -e $? -n "mnist_train" >/dev/null 2>&1
-
-python -m paddle.utils.plotcurve -i $log > plot.png

v1_api_demo/mnist/vgg_16_mnist.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-is_predict = get_config_arg("is_predict", bool, False)
-
-####################Data Configuration ##################
-
-if not is_predict:
-    data_dir = './data/'
-    define_py_data_sources2(
-        train_list=data_dir + 'train.list',
-        test_list=data_dir + 'test.list',
-        module='mnist_provider',
-        obj='process')
-
-######################Algorithm Configuration #############
-settings(
-    batch_size=128,
-    learning_rate=0.1 / 128.0,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * 128))
-
-#######################Network Configuration #############
-
-data_size = 1 * 28 * 28
-label_size = 10
-img = data_layer(name='pixel', size=data_size)
-
-# small_vgg is predined in trainer_config_helpers.network
-predict = small_vgg(input_image=img, num_channels=1, num_classes=label_size)
-
-if not is_predict:
-    lbl = data_layer(name="label", size=label_size)
-    inputs(img, lbl)
-    outputs(classification_cost(input=predict, label=lbl))
-else:
-    outputs(predict)

v1_api_demo/model_zoo/embedding/.gitignore

-baidu.dict
-model_*.emb

v1_api_demo/model_zoo/embedding/extract_para.py

-#!/bin/env python
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Example:
-    python extract_para.py --preModel PREMODEL --preDict PREDICT \
-                            --usrModel USRMODEL --usrDict USRDICT -d DIM
-
-Options:
-    -h, --help          show this help message and exit
-    --preModel PREMODEL the name of pretrained embedding model
-    --preDict PREDICT   the name of pretrained dictionary
-    --usrModel usrModel the name of output usr embedding model
-    --usrDict usrDict   the name of user specified dictionary
-    -d DIM              dimension of parameter
-"""
-from optparse import OptionParser
-import struct
-
-
-def get_row_index(preDict, usrDict):
-    """
-    Get the row positions for all words in user dictionary from pre-trained dictionary.
-    return: a list of row positions
-    Example: preDict='a\nb\nc\n', usrDict='a\nc\n', then return [0,2]
-    """
-    pos = []
-    index = dict()
-    with open(preDict, "r") as f:
-        for line_index, line in enumerate(f):
-            word = line.strip().split()[0]
-            index[word] = line_index
-    with open(usrDict, "r") as f:
-        for line in f:
-            word = line.strip().split()[0]
-            pos.append(index[word])
-    return pos
-
-
-def extract_parameters_by_usrDict(preModel, preDict, usrModel, usrDict,
-                                  paraDim):
-    """
-    Extract desired parameters from a pretrained embedding model based on user dictionary
-    """
-    if paraDim not in [32, 64, 128, 256]:
-        raise RuntimeError("We only support 32, 64, 128, 256 dimensions now")
-
-    fi = open(preModel, "rb")
-    fo = open(usrModel, "wb")
-
-    # write filehead
-    rowIndex = get_row_index(preDict, usrDict)
-    newHead = struct.pack("iil", 0, 4, len(rowIndex) * paraDim)
-    fo.write(newHead)
-    bytes = 4 * paraDim
-    for i in range(0, len(rowIndex)):
-        # find the absolute position of input file
-        fi.seek(rowIndex[i] * bytes + 16, 0)
-        fo.write(fi.read(bytes))
-
-    print "extract parameters finish, total", len(rowIndex), "lines"
-    fi.close()
-
-
-def main():
-    """
-    Main entry for running paraconvert.py 
-    """
-    usage = "usage: \n" \
-            "python %prog --preModel PREMODEL --preDict PREDICT" \
-            " --usrModel USRMODEL --usrDict USRDICT -d DIM"
-    parser = OptionParser(usage)
-    parser.add_option(
-        "--preModel",
-        action="store",
-        dest="preModel",
-        help="the name of pretrained embedding model")
-    parser.add_option(
-        "--preDict",
-        action="store",
-        dest="preDict",
-        help="the name of pretrained dictionary")
-    parser.add_option(
-        "--usrModel",
-        action="store",
-        dest="usrModel",
-        help="the name of output usr embedding model")
-    parser.add_option(
-        "--usrDict",
-        action="store",
-        dest="usrDict",
-        help="the name of user specified dictionary")
-    parser.add_option(
-        "-d", action="store", dest="dim", help="dimension of parameter")
-    (options, args) = parser.parse_args()
-    extract_parameters_by_usrDict(options.preModel, options.preDict,
-                                  options.usrModel, options.usrDict,
-                                  int(options.dim))
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/model_zoo/embedding/paraconvert.py

-#!/bin/env python
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Example:
-    python paraconvert.py --b2t -i INPUT -o OUTPUT -d DIM
-    python paraconvert.py --t2b -i INPUT -o OUTPUT
-
-Options:
-    -h, --help  show this help message and exit
-    --b2t       convert parameter file of embedding model from binary to text
-    --t2b       convert parameter file of embedding model from text to binary
-    -i INPUT    input parameter file name
-    -o OUTPUT   output parameter file name
-    -d DIM      dimension of parameter
-"""
-from optparse import OptionParser
-import struct
-
-
-def binary2text(input, output, paraDim):
-    """
-    Convert a binary parameter file of embedding model to be a text file.  
-    input: the name of input binary parameter file, the format is:
-           1) the first 16 bytes is filehead:
-                version(4 bytes): version of paddle, default = 0
-                floatSize(4 bytes): sizeof(float) = 4
-                paraCount(8 bytes): total number of parameter
-           2) the next (paraCount * 4) bytes is parameters, each has 4 bytes 
-    output: the name of output text parameter file, for example:
-           0,4,32156096
-           -0.7845433,1.1937413,-0.1704215,...
-           0.0000909,0.0009465,-0.0008813,...
-           ...
-           the format is:
-           1) the first line is filehead: 
-              version=0, floatSize=4, paraCount=32156096
-           2) other lines print the paramters
-              a) each line prints paraDim paramters splitted by ','
-              b) there is paraCount/paraDim lines (embedding words)
-    paraDim: dimension of parameters 
-    """
-    fi = open(input, "rb")
-    fo = open(output, "w")
-    """
-    """
-    version, floatSize, paraCount = struct.unpack("iil", fi.read(16))
-    newHead = ','.join([str(version), str(floatSize), str(paraCount)])
-    print >> fo, newHead
-
-    bytes = 4 * int(paraDim)
-    format = "%df" % int(paraDim)
-    context = fi.read(bytes)
-    line = 0
-
-    while context:
-        numbers = struct.unpack(format, context)
-        lst = []
-        for i in numbers:
-            lst.append('%8.7f' % i)
-        print >> fo, ','.join(lst)
-        context = fi.read(bytes)
-        line += 1
-    fi.close()
-    fo.close()
-    print "binary2text finish, total", line, "lines"
-
-
-def get_para_count(input):
-    """
-    Compute the total number of embedding parameters in input text file. 
-    input: the name of input text file
-    """
-    numRows = 1
-    paraDim = 0
-    with open(input) as f:
-        line = f.readline()
-        paraDim = len(line.split(","))
-        for line in f:
-            numRows += 1
-    return numRows * paraDim
-
-
-def text2binary(input, output, paddle_head=True):
-    """
-    Convert a text parameter file of embedding model to be a binary file.
-    input: the name of input text parameter file, for example:
-           -0.7845433,1.1937413,-0.1704215,...
-           0.0000909,0.0009465,-0.0008813,... 
-           ...
-           the format is:
-           1) it doesn't have filehead
-           2) each line stores the same dimension of parameters, 
-              the separator is commas ','
-    output: the name of output binary parameter file, the format is:
-           1) the first 16 bytes is filehead: 
-             version(4 bytes), floatSize(4 bytes), paraCount(8 bytes)
-           2) the next (paraCount * 4) bytes is parameters, each has 4 bytes
-    """
-    fi = open(input, "r")
-    fo = open(output, "wb")
-
-    newHead = struct.pack("iil", 0, 4, get_para_count(input))
-    fo.write(newHead)
-
-    count = 0
-    for line in fi:
-        line = line.strip().split(",")
-        for i in range(0, len(line)):
-            binary_data = struct.pack("f", float(line[i]))
-            fo.write(binary_data)
-        count += 1
-    fi.close()
-    fo.close()
-    print "text2binary finish, total", count, "lines"
-
-
-def main():
-    """
-    Main entry for running paraconvert.py 
-    """
-    usage = "usage: \n" \
-            "python %prog --b2t -i INPUT -o OUTPUT -d DIM \n" \
-            "python %prog --t2b -i INPUT -o OUTPUT"
-    parser = OptionParser(usage)
-    parser.add_option(
-        "--b2t",
-        action="store_true",
-        help="convert parameter file of embedding model from binary to text")
-    parser.add_option(
-        "--t2b",
-        action="store_true",
-        help="convert parameter file of embedding model from text to binary")
-    parser.add_option(
-        "-i", action="store", dest="input", help="input parameter file name")
-    parser.add_option(
-        "-o", action="store", dest="output", help="output parameter file name")
-    parser.add_option(
-        "-d", action="store", dest="dim", help="dimension of parameter")
-    (options, args) = parser.parse_args()
-    if options.b2t:
-        binary2text(options.input, options.output, options.dim)
-    if options.t2b:
-        text2binary(options.input, options.output)
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/model_zoo/embedding/pre_DictAndModel.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-set -x
-BASE_URL='http://paddlepaddle.cdn.bcebos.com/model_zoo/embedding'
-
-DOWNLOAD_ITEMS=(baidu.dict model_32.emb model_64.emb model_128.emb model_256.emb)
-ITEM_MD5=(fa03a12321eaab6c30a8fcc9442eaea3
-          f88c8325ee6da6187f1080e8fe66c1cd
-          927cf70f27f860aff1a5703ebf7f1584
-	  a52e43655cd25d279777ed509a1ae27b
-	  b92c67fe9ff70fea53596080e351ac80)
-
-for ((i=0; i<${#ITEM_MD5[@]}; i++))
-do
-  FILENAME=${DOWNLOAD_ITEMS[${i}]}
-  REAL_MD5=`wget ${BASE_URL}/${FILENAME} -O - | tee ${FILENAME} | md5sum | cut -d ' ' -f 1`
-  EXPECTED_MD5=${ITEM_MD5[${i}]}
-  [ "${EXPECTED_MD5}" = "${REAL_MD5}" ]
-done

v1_api_demo/model_zoo/resnet/.gitignore

-fea_output/
-features/
-model.list
-ResNet_50.dot
-ResNet_50.png

v1_api_demo/model_zoo/resnet/classify.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import cPickle
-import logging
-from PIL import Image
-import numpy as np
-from optparse import OptionParser
-
-import paddle.utils.image_util as image_util
-
-from py_paddle import swig_paddle, DataProviderConverter
-from paddle.trainer.PyDataProvider2 import dense_vector
-from paddle.trainer.config_parser import parse_config
-
-logging.basicConfig(
-    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
-logging.getLogger().setLevel(logging.INFO)
-
-
-class ImageClassifier():
-    def __init__(self,
-                 train_conf,
-                 model_dir=None,
-                 resize_dim=256,
-                 crop_dim=224,
-                 use_gpu=True,
-                 mean_file=None,
-                 output_layer=None,
-                 oversample=False,
-                 is_color=True):
-        """
-        train_conf: network configure.
-        model_dir: string, directory of model.
-        resize_dim: int, resized image size.
-        crop_dim: int, crop size.
-        mean_file: string, image mean file.
-        oversample: bool, oversample means multiple crops, namely five
-                    patches (the four corner patches and the center
-                    patch) as well as their horizontal reflections,
-                    ten crops in all.
-        """
-        self.train_conf = train_conf
-        self.model_dir = model_dir
-        if model_dir is None:
-            self.model_dir = os.path.dirname(train_conf)
-
-        self.resize_dim = resize_dim
-        self.crop_dims = [crop_dim, crop_dim]
-        self.oversample = oversample
-        self.is_color = is_color
-
-        self.output_layer = output_layer
-        if self.output_layer:
-            assert isinstance(self.output_layer, basestring)
-            self.output_layer = self.output_layer.split(",")
-
-        self.transformer = image_util.ImageTransformer(is_color=is_color)
-        self.transformer.set_transpose((2, 0, 1))
-        self.transformer.set_channel_swap((2, 1, 0))
-
-        self.mean_file = mean_file
-        if self.mean_file is not None:
-            mean = np.load(self.mean_file)['data_mean']
-            mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
-            self.transformer.set_mean(mean)  # mean pixel
-        else:
-            # if you use three mean value, set like:
-            # this three mean value is calculated from ImageNet.
-            self.transformer.set_mean(np.array([103.939, 116.779, 123.68]))
-
-        conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (int(use_gpu))
-        conf = parse_config(train_conf, conf_args)
-        swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
-        self.network = swig_paddle.GradientMachine.createFromConfigProto(
-            conf.model_config)
-        assert isinstance(self.network, swig_paddle.GradientMachine)
-        self.network.loadParameters(self.model_dir)
-
-        data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
-        slots = [dense_vector(data_size)]
-        self.converter = DataProviderConverter(slots)
-
-    def get_data(self, img_path):
-        """
-        1. load image from img_path.
-        2. resize or oversampling.
-        3. transformer data: transpose, channel swap, sub mean.
-        return K x H x W ndarray.
-
-        img_path: image path.
-        """
-        image = image_util.load_image(img_path, self.is_color)
-        # Another way to extract oversampled features is that
-        # cropping and averaging from large feature map which is
-        # calculated by large size of image.
-        # This way reduces the computation.
-        if self.oversample:
-            # image_util.resize_image: short side is self.resize_dim
-            image = image_util.resize_image(image, self.resize_dim)
-            image = np.array(image)
-            input = np.zeros(
-                (1, image.shape[0], image.shape[1], 3), dtype=np.float32)
-            input[0] = image.astype(np.float32)
-            input = image_util.oversample(input, self.crop_dims)
-        else:
-            image = image.resize(self.crop_dims, Image.ANTIALIAS)
-            input = np.zeros(
-                (1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
-            input[0] = np.array(image).astype(np.float32)
-
-        data_in = []
-        for img in input:
-            img = self.transformer.transformer(img).flatten()
-            data_in.append([img.tolist()])
-        # paddle input: [[[]],[[]],...], [[]] is one sample.
-        return data_in
-
-    def forward(self, input_data):
-        """
-        return output arguments which are the Outputs() in network configure.
-
-        input_data: py_paddle input data.
-        call forward.
-        """
-        in_arg = self.converter(input_data)
-        return self.network.forwardTest(in_arg)
-
-    def forward(self, data, output_layer):
-        """
-        return output arguments which are the Outputs() in network configure.
-
-        input_data: py_paddle input data.
-        call forward.
-        """
-        input = self.converter(data)
-        self.network.forwardTest(input)
-        output = self.network.getLayerOutputs(output_layer)
-        res = {}
-        if isinstance(output_layer, basestring):
-            output_layer = [output_layer]
-        for name in output_layer:
-            # For oversampling, average predictions across crops.
-            # If not, the shape of output[name]: (1, class_number),
-            # the mean is also applicable.
-            res[name] = output[name]['value'].mean(0)
-
-        return res
-
-    def predict(self, data_file):
-        """
-        call forward and predicting.
-
-        data_file: input image list.
-        """
-        image_files = open(data_file, 'rb').readlines()
-        results = {}
-        if self.output_layer is None:
-            self.output_layer = ["output"]
-        for line in image_files:
-            image = line.split()[0]
-            data = self.get_data(image)
-            prob = self.forward(data, self.output_layer)
-            lab = np.argsort(-prob[self.output_layer[0]])
-            results[image] = lab[0]
-            logging.info("Label of %s is: %d", image, lab[0])
-        return results
-
-    def extract(self, data_file, output_dir, batch_size=10000):
-        """
-        extract and save features of output layers, which are
-        specify in Outputs() in network configure.
-
-        data_file: file name of input data.
-        output_dir: saved directory of extracted features.
-        batch_size: sample number of one batch file.
-        """
-        if not os.path.exists(output_dir):
-            os.mkdir(output_dir)
-
-        sample_num = 0
-        batch_num = 0
-        image_feature = {}
-        image_files = open(data_file, 'rb').readlines()
-        for idx, line in enumerate(image_files):
-            image = line.split()[0]
-            data = self.get_data(image)
-            feature = self.forward(data, self.output_layer)
-            # save extracted features
-            file_name = image.split("/")[-1]
-            image_feature[file_name] = feature
-            sample_num += 1
-            if sample_num == batch_size:
-                batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
-                self.save_file(image_feature, batch_name)
-                logging.info('Finish batch %d', batch_num)
-                batch_num += 1
-                sample_num = 0
-                image_feature = {}
-            if idx % 1000 == 0:
-                logging.info('%d/%d, %s', idx, len(image_files), file_name)
-        if sample_num > 0:
-            batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
-            self.save_file(image_feature, batch_name)
-            logging.info('Finish batch %d', batch_num)
-        logging.info('Done: make image feature batch')
-
-    def save_file(self, data, file):
-        of = open(file, 'wb')
-        cPickle.dump(data, of, protocol=cPickle.HIGHEST_PROTOCOL)
-
-
-def option_parser():
-    """
-    Main entry for predciting
-    """
-    usage = "%prog -c config -i data_list -w model_dir [options]"
-    parser = OptionParser(usage="usage: %s" % usage)
-    parser.add_option(
-        "-j",
-        "--job",
-        action="store",
-        dest="job_type",
-        help="job type: predict, extract\
-                            predict: predicting,\
-                            extract: extract features")
-    parser.add_option(
-        "-c",
-        "--conf",
-        action="store",
-        dest="train_conf",
-        help="network config")
-    parser.add_option(
-        "-i", "--data", action="store", dest="data_file", help="image list")
-    parser.add_option(
-        "-w",
-        "--model",
-        action="store",
-        dest="model_path",
-        default=None,
-        help="model path")
-    parser.add_option(
-        "-g",
-        "--use_gpu",
-        action="store",
-        dest="use_gpu",
-        default=True,
-        help="Whether to use gpu mode.")
-    parser.add_option(
-        "-o",
-        "--output_dir",
-        action="store",
-        dest="output_dir",
-        default="output",
-        help="output path")
-    parser.add_option(
-        "-m",
-        "--mean",
-        action="store",
-        dest="mean",
-        default=None,
-        help="mean file.")
-    parser.add_option(
-        "-p",
-        "--multi_crop",
-        action="store_true",
-        dest="multi_crop",
-        default=False,
-        help="Wether to use multiple crops on image.")
-    parser.add_option("-l", "--output_layer", action="store",
-                      dest="output_layer", default=None,
-                      help="--job=extract, specify layers to extract "\
-                           "features, --job=predict, specify layer of "
-                           "classification probability, output in resnet.py.")
-    return parser.parse_args()
-
-
-def main():
-    """
-    1. parse input arguments.
-    2. predicting or extract features according job type.
-    """
-    options, args = option_parser()
-    obj = ImageClassifier(
-        options.train_conf,
-        options.model_path,
-        use_gpu=options.use_gpu,
-        mean_file=options.mean,
-        output_layer=options.output_layer,
-        oversample=options.multi_crop)
-    if options.job_type == "predict":
-        obj.predict(options.data_file)
-
-    elif options.job_type == "extract":
-        obj.extract(options.data_file, options.output_dir)
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/model_zoo/resnet/example/.gitignore

-*image_list_provider_copy_1.py

v1_api_demo/model_zoo/resnet/example/__init__.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.

v1_api_demo/model_zoo/resnet/example/image_list_provider.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.utils.image_util import *
-from paddle.trainer.PyDataProvider2 import *
-
-
-def hook(settings, image_size, crop_size, color, file_list, is_train, **kwargs):
-    """
-    Description: Init with a list of data file
-    file_list is the name list of input files.
-    kwargs["load_data_args"] is the value of 'load_data_args'
-    which can be set in config.
-    Each args is separated by a column.
-    image_size: the crop image size.
-    mean_meta: the path of the meta file to store the mean image.
-    mean_value: can be mean value, not a file.
-                can not set mean_meta and mean_value at the same time.
-    color: 'color' means a color image. Otherwise, it means a gray image.
-    is_train: whether the data provider is used for training.
-              Data argumentation might be different for training and testing.
-    """
-    settings.img_size = image_size
-    settings.crop_size = crop_size
-    settings.mean_img_size = settings.crop_size
-    settings.color = color  # default is color
-    settings.is_train = is_train
-
-    settings.is_swap_channel = kwargs.get('swap_channel', None)
-    if settings.is_swap_channel is not None:
-        settings.swap_channel = settings.is_swap_channel
-        settings.is_swap_channel = True
-
-    if settings.color:
-        settings.img_input_size = settings.crop_size * settings.crop_size * 3
-    else:
-        settings.img_input_size = settings.crop_size * settings.crop_size
-
-    settings.file_list = file_list
-    settings.mean_meta = kwargs.get('mean_meta', None)
-    settings.mean_value = kwargs.get('mean_value', None)
-    # can not specify both mean_meta and mean_value.
-    assert not (settings.mean_meta and settings.mean_value)
-    if not settings.mean_meta:
-        settings.mean_value = kwargs.get('mean_value')
-        sz = settings.crop_size * settings.crop_size
-        settings.img_mean = np.zeros(sz * 3, dtype=np.single)
-        for idx, value in enumerate(settings.mean_value):
-            settings.img_mean[idx * sz:(idx + 1) * sz] = value
-        settings.img_mean = settings.img_mean.reshape(3, settings.crop_size,
-                                                      settings.crop_size)
-
-    else:
-        settings.img_mean = load_meta(settings.mean_meta,
-                                      settings.mean_img_size,
-                                      settings.crop_size, settings.color)
-
-    settings.input_types = [
-        dense_vector(settings.img_input_size),  # image feature
-        integer_value(1)
-    ]  # labels
-
-    settings.logger.info('Image short side: %s', settings.img_size)
-    settings.logger.info('Crop size: %s', settings.crop_size)
-    settings.logger.info('Meta path: %s', settings.mean_meta)
-    if settings.is_swap_channel:
-        settings.logger.info('swap channel: %s', settings.swap_channel)
-    settings.logger.info('DataProvider Initialization finished')
-
-
-@provider(init_hook=hook, should_shuffle=False)
-def processData(settings, file_list):
-    """
-    The main function for loading data.
-    Load the batch, iterate all the images and labels in this batch.
-    file_name: the batch file name.
-    """
-    img_path, lab = file_list.strip().split(' ')
-    img = Image.open(img_path)
-    img.load()
-    img = img.resize((settings.img_size, settings.img_size), Image.ANTIALIAS)
-    img = np.array(img).astype(np.float32)
-    if len(img.shape) == 3:
-        img = np.swapaxes(img, 1, 2)
-        img = np.swapaxes(img, 1, 0)
-    # swap channel
-    if settings.is_swap_channel:
-        img = img[settings.swap_channel, :, :]
-    img_feat = preprocess_img(img, settings.img_mean, settings.crop_size,
-                              settings.is_train, settings.color)
-    yield img_feat.tolist(), int(lab.strip())

v1_api_demo/model_zoo/resnet/example/test.list

-example/dog.jpg 0
-example/cat.jpg 0

v1_api_demo/model_zoo/resnet/extract_fea_c++.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-#set names of layer which you want to extract feature
-#in Outputs() of resnet.py 
-#like: Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")
-layer_num=50
-configure=./resnet.py
-model_path=./model/resnet_$layer_num
-fea_dir=fea_output
-#Output is text file.
-#Each line is one sample's features.
-#If you set N layer names in Outputs()
-#each line contains N features sperated by ";". 
-
-# create model list file.
-model_list=./model.list
-touch $model_list | echo $model_path > $model_list
-
-paddle train \
-  --local=true \
-  --job=test \
-  --config=$configure \
-  --model_list=$model_list \
-  --use_gpu=1 \
-  --predict_output_dir=$fea_dir \
-  --config_args=is_test=1,layer_num=$layer_num

v1_api_demo/model_zoo/resnet/extract_fea_py.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-#Note if you use CPU mode, you need to set use_gpu=0 in classify.py. like this:
-#conf_args = "is_test=0,use_gpu=1,is_predict=1"
-#conf = parse_config(train_conf, conf_args)
-#swig_paddle.initPaddle("--use_gpu=0")
-python classify.py \
-     --job=extract \
-     --conf=resnet.py \
-     --use_gpu=1 \
-     --mean=model/mean_meta_224/mean.meta \
-     --model=model/resnet_50 \
-     --data=./example/test.list \
-     --output_layer="res5_3_branch2c_conv,res5_3_branch2c_bn" \
-     --output_dir=features

v1_api_demo/model_zoo/resnet/get_model.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-mkdir model
-cd model
-
-echo "Downloading ResNet models..."
-
-for file in resnet_50.tar.gz resnet_101.tar.gz resnet_152.tar.gz mean_meta_224.tar.gz 
-do 
-  wget http://paddlepaddle.bj.bcebos.com/model_zoo/imagenet/$file
-  tar -xvf $file 
-  rm $file
-done
-
-echo "Done."

v1_api_demo/model_zoo/resnet/load_feature.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import cPickle
-import logging
-
-logging.basicConfig(
-    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
-logging.getLogger().setLevel(logging.INFO)
-
-
-def load_feature_c(file):
-    """
-    Load feature extracted by C++ interface.
-    Return a list.
-    file: feature file.
-    """
-    features = []
-    f = open(file, 'r')
-    for line in f:
-        sample = []
-        for slot in line.strip().split(";"):
-            fea = [float(val) for val in slot.strip().split()]
-            if fea:
-                sample.append(fea)
-        features.append(sample)
-    f.close()
-    return features
-
-
-def load_feature_py(feature_dir):
-    """
-    Load feature extracted by python interface.
-    Return a dictionary.
-    feature_dir: directory of feature file.
-    """
-    file_list = os.listdir(feature_dir)
-    file_list = [os.path.join(feature_dir, f) for f in file_list]
-    features = {}
-    for file_name in file_list:
-        with open(file_name, 'rb') as f:
-            feature = cPickle.load(f)
-            features.update(feature)
-            logging.info('Load feature file %s', file_name)
-    return features
-
-
-if __name__ == '__main__':
-    print load_feature_py(sys.argv[1])
-    #print load_feature_c(sys.argv[1]) 

v1_api_demo/model_zoo/resnet/net_diagram.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-:'
-Visual deep residual network
-1. Using make_model_diagram.py to generate dot file.
-2. Using graphviz to convert dot file.
-
-Usage:
-./net_diagram.sh
-'
-
-set -e
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-img_type=png
-img_fileprefix=ResNet_50
-conf_filename=resnet.py
-dot_filename=ResNet_50.dot
-config_str="layer_num=50,data_provider=0"
-
-python -m paddle.utils.make_model_diagram $conf_filename $dot_filename $config_str
-
-# If you have installed graphviz, running like this:
-# dot -Tpng -o ResNet.png ResNet.dot

v1_api_demo/model_zoo/resnet/predict.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-python classify.py \
-     --job=predict \
-     --conf=resnet.py\
-     --model=model/resnet_50 \
-     --multi_crop \
-     --use_gpu=1 \
-     --data=./example/test.list

v1_api_demo/model_zoo/resnet/resnet.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-"""
-paper: https://arxiv.org/abs/1512.03385
-"""
-is_test = get_config_arg("is_test", bool, False)
-is_predict = get_config_arg("is_predict", bool, False)
-data_provider = get_config_arg("data_provider", bool, True)
-layer_num = get_config_arg("layer_num", int, 50)
-
-if not is_predict and data_provider:
-    train_list = 'train.list' if not is_test else None
-    # mean.meta is mean file of ImageNet dataset.
-    # mean.meta size : 3 x 224 x 224.
-    # If you use three mean value, set like:
-    # "mean_value:103.939,116.779,123.68;"
-    args = {
-        'mean_meta': "model/mean_meta_224/mean.meta",
-        'image_size': 224,
-        'crop_size': 224,
-        'color': True,
-        'swap_channel:': [2, 1, 0]
-    }
-    define_py_data_sources2(
-        train_list,
-        'example/test.list',
-        module="example.image_list_provider",
-        obj="processData",
-        args=args)
-
-batch_size = 1
-learning_rate = 0.1 / batch_size
-momentum = 0.9
-weight_decay = 0.0001 * batch_size
-default_momentum(momentum)
-default_decay_rate(weight_decay)
-
-Settings(
-    algorithm='sgd',
-    batch_size=batch_size,
-    learning_rate=learning_rate,
-
-    # set the appropriate parameters according your schedule
-    learning_method='momentum',
-    learning_rate_decay_a=0.5,
-    learning_rate_decay_b=1200000 * 10,
-    learning_rate_schedule="discexp", )
-
-
-def conv_bn_layer(name,
-                  input,
-                  filter_size,
-                  num_filters,
-                  stride,
-                  padding,
-                  channels=None,
-                  active_type=ReluActivation()):
-    """
-    A wrapper for conv layer with batch normalization layers.
-    Note:
-    conv layer has no activation.
-    """
-
-    tmp = img_conv_layer(
-        name=name + "_conv",
-        input=input,
-        filter_size=filter_size,
-        num_channels=channels,
-        num_filters=num_filters,
-        stride=stride,
-        padding=padding,
-        act=LinearActivation(),
-        bias_attr=False)
-    return batch_norm_layer(
-        name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
-
-
-def bottleneck_block(name, input, num_filters1, num_filters2):
-    """
-    A wrapper for bottlenect building block in ResNet.
-    Last conv_bn_layer has no activation.
-    Addto layer has activation of relu.
-    """
-    last_name = conv_bn_layer(
-        name=name + '_branch2a',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters1,
-        stride=1,
-        padding=0)
-    last_name = conv_bn_layer(
-        name=name + '_branch2b',
-        input=last_name,
-        filter_size=3,
-        num_filters=num_filters1,
-        stride=1,
-        padding=1)
-    last_name = conv_bn_layer(
-        name=name + '_branch2c',
-        input=last_name,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=1,
-        padding=0,
-        active_type=LinearActivation())
-
-    return addto_layer(
-        name=name + "_addto", input=[input, last_name], act=ReluActivation())
-
-
-def mid_projection(name, input, num_filters1, num_filters2, stride=2):
-    """
-    A wrapper for middile projection in ResNet.
-    projection shortcuts are used for increasing dimensions,
-    and other shortcuts are identity
-    branch1: projection shortcuts are used for increasing
-    dimensions, has no activation.
-    branch2x: bottleneck building block, shortcuts are identity.
-    """
-    # stride = 2
-    branch1 = conv_bn_layer(
-        name=name + '_branch1',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=stride,
-        padding=0,
-        active_type=LinearActivation())
-
-    last_name = conv_bn_layer(
-        name=name + '_branch2a',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters1,
-        stride=stride,
-        padding=0)
-    last_name = conv_bn_layer(
-        name=name + '_branch2b',
-        input=last_name,
-        filter_size=3,
-        num_filters=num_filters1,
-        stride=1,
-        padding=1)
-
-    last_name = conv_bn_layer(
-        name=name + '_branch2c',
-        input=last_name,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=1,
-        padding=0,
-        active_type=LinearActivation())
-
-    return addto_layer(
-        name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
-
-
-def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
-    """
-    A wrapper for 50,101,152 layers of ResNet.
-    res2_num: number of blocks stacked in conv2_x
-    res3_num: number of blocks stacked in conv3_x
-    res4_num: number of blocks stacked in conv4_x
-    res5_num: number of blocks stacked in conv5_x
-    """
-    # For ImageNet
-    # conv1: 112x112
-    img = data_layer(name='input', size=224 * 224 * 3)
-    tmp = conv_bn_layer(
-        "conv1",
-        img,
-        filter_size=7,
-        channels=3,
-        num_filters=64,
-        stride=2,
-        padding=3)
-    tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
-
-    # conv2_x: 56x56
-    tmp = mid_projection(
-        name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
-    for i in xrange(2, res2_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
-
-    # conv3_x: 28x28
-    tmp = mid_projection(
-        name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
-    for i in xrange(2, res3_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res3_" + str(i),
-            input=tmp,
-            num_filters1=128,
-            num_filters2=512)
-
-    # conv4_x: 14x14
-    tmp = mid_projection(
-        name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
-    for i in xrange(2, res4_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res4_" + str(i),
-            input=tmp,
-            num_filters1=256,
-            num_filters2=1024)
-
-    # conv5_x: 7x7
-    tmp = mid_projection(
-        name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
-    for i in xrange(2, res5_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res5_" + str(i),
-            input=tmp,
-            num_filters1=512,
-            num_filters2=2048)
-
-    tmp = img_pool_layer(
-        name='avgpool',
-        input=tmp,
-        pool_size=7,
-        stride=1,
-        pool_type=AvgPooling())
-
-    output = fc_layer(
-        name='output', input=tmp, size=1000, act=SoftmaxActivation())
-
-    if not is_predict:
-        classification_cost(
-            input=output, label=data_layer(
-                name='label', size=1))
-
-
-def res_net_50():
-    deep_res_net(3, 4, 6, 3)
-
-
-def res_net_101():
-    deep_res_net(3, 4, 23, 3)
-
-
-def res_net_152():
-    deep_res_net(3, 8, 36, 3)
-
-
-if not is_predict:
-    Inputs("input", "label")
-else:
-    Inputs("input")
-# Outputs("cost-softmax" if not is_predict else "output")
-Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")
-
-if layer_num == 50:
-    res_net_50()
-elif layer_num == 101:
-    res_net_101()
-elif layer_num == 152:
-    res_net_152()
-else:
-    print("Wrong layer number.")

v1_api_demo/quick_start/.gitignore

-*.pyc
-data/dict.txt
-data/dict_all.txt
-data/labels.list
-data/mosesdecoder-master/
-data/reviews_Electronics_5.json.gz
-data/test.list
-data/test.txt
-data/train.list
-data/train.txt
-data/pred.list
-data/pred.txt
-dataprovider_copy_1.py
-train.log
-output

v1_api_demo/quick_start/api_predict.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os, sys
-import numpy as np
-from optparse import OptionParser
-from py_paddle import swig_paddle, DataProviderConverter
-from paddle.trainer.PyDataProvider2 import sparse_binary_vector
-from paddle.trainer.config_parser import parse_config
-"""
-Usage: run following command to show help message.
-  python api_predict.py -h
-"""
-
-
-class QuickStartPrediction():
-    def __init__(self, train_conf, dict_file, model_dir=None, label_file=None):
-        """
-        train_conf: trainer configure.
-        dict_file: word dictionary file name.
-        model_dir: directory of model.
-        """
-        self.train_conf = train_conf
-        self.dict_file = dict_file
-        self.word_dict = {}
-        self.dict_dim = self.load_dict()
-        self.model_dir = model_dir
-        if model_dir is None:
-            self.model_dir = os.path.dirname(train_conf)
-
-        self.label = None
-        if label_file is not None:
-            self.load_label(label_file)
-
-        conf = parse_config(train_conf, "is_predict=1")
-        self.network = swig_paddle.GradientMachine.createFromConfigProto(
-            conf.model_config)
-        self.network.loadParameters(self.model_dir)
-        input_types = [sparse_binary_vector(self.dict_dim)]
-        self.converter = DataProviderConverter(input_types)
-
-    def load_dict(self):
-        """
-        Load dictionary from self.dict_file.
-        """
-        for line_count, line in enumerate(open(self.dict_file, 'r')):
-            self.word_dict[line.strip().split('\t')[0]] = line_count
-        return len(self.word_dict)
-
-    def load_label(self, label_file):
-        """
-        Load label.
-        """
-        self.label = {}
-        for v in open(label_file, 'r'):
-            self.label[int(v.split('\t')[1])] = v.split('\t')[0]
-
-    def get_index(self, data):
-        """
-        transform word into integer index according to the dictionary.
-        """
-        words = data.strip().split()
-        word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
-        return word_slot
-
-    def batch_predict(self, data_batch):
-        input = self.converter(data_batch)
-        output = self.network.forwardTest(input)
-        prob = output[0]["id"].tolist()
-        print("predicting labels is:")
-        print prob
-
-
-def option_parser():
-    usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
-    parser = OptionParser(usage="usage: %s [options]" % usage)
-    parser.add_option(
-        "-n",
-        "--tconf",
-        action="store",
-        dest="train_conf",
-        help="network config")
-    parser.add_option(
-        "-d",
-        "--dict",
-        action="store",
-        dest="dict_file",
-        help="dictionary file")
-    parser.add_option(
-        "-b",
-        "--label",
-        action="store",
-        dest="label",
-        default=None,
-        help="dictionary file")
-    parser.add_option(
-        "-c",
-        "--batch_size",
-        type="int",
-        action="store",
-        dest="batch_size",
-        default=1,
-        help="the batch size for prediction")
-    parser.add_option(
-        "-w",
-        "--model",
-        action="store",
-        dest="model_path",
-        default=None,
-        help="model path")
-    return parser.parse_args()
-
-
-def main():
-    options, args = option_parser()
-    train_conf = options.train_conf
-    batch_size = options.batch_size
-    dict_file = options.dict_file
-    model_path = options.model_path
-    label = options.label
-    swig_paddle.initPaddle("--use_gpu=0")
-    predict = QuickStartPrediction(train_conf, dict_file, model_path, label)
-
-    batch = []
-    labels = []
-    for line in sys.stdin:
-        [label, text] = line.split("\t")
-        labels.append(int(label))
-        batch.append([predict.get_index(text)])
-    print("labels is:")
-    print labels
-    predict.batch_predict(batch)
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/quick_start/api_predict.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-#Note the default model is pass-00002, you shold make sure the model path
-#exists or change the mode path.
-#only test on trainer_config.lr.py
-model=output/model/pass-00001/
-config=trainer_config.lr.py
-label=data/labels.list
-dict=data/dict.txt
-batch_size=20
-head -n$batch_size data/test.txt | python api_predict.py \
-     --tconf=$config\
-     --model=$model \
-     --label=$label \
-     --dict=$dict \
-     --batch_size=$batch_size

v1_api_demo/quick_start/api_train.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import itertools
-import random
-
-from paddle.trainer.config_parser import parse_config
-from py_paddle import swig_paddle as api
-from py_paddle import DataProviderConverter
-from paddle.trainer.PyDataProvider2 \
-    import integer_value, integer_value_sequence, sparse_binary_vector
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--train_data", type=str, required=False, help="train data file")
-    parser.add_argument("--test_data", type=str, help="test data file")
-    parser.add_argument(
-        "--config", type=str, required=True, help="config file name")
-    parser.add_argument("--dict_file", required=True, help="dictionary file")
-    parser.add_argument(
-        "--seq", default=1, type=int, help="whether use sequence training")
-    parser.add_argument(
-        "--use_gpu", default=0, type=int, help="whether use GPU for training")
-    parser.add_argument(
-        "--trainer_count",
-        default=1,
-        type=int,
-        help="Number of threads for training")
-    parser.add_argument(
-        "--num_passes", default=5, type=int, help="Number of training passes")
-    return parser.parse_args()
-
-
-UNK_IDX = 0
-
-
-def load_data(file_name, word_dict):
-    with open(file_name, 'r') as f:
-        for line in f:
-            label, comment = line.strip().split('\t')
-            words = comment.split()
-            word_slot = [word_dict.get(w, UNK_IDX) for w in words]
-            yield word_slot, int(label)
-
-
-def load_dict(dict_file):
-    word_dict = dict()
-    with open(dict_file, 'r') as f:
-        for i, line in enumerate(f):
-            w = line.strip().split()[0]
-            word_dict[w] = i
-    return word_dict
-
-
-def main():
-    options = parse_arguments()
-    api.initPaddle("--use_gpu=%s" % options.use_gpu,
-                   "--trainer_count=%s" % options.trainer_count)
-
-    word_dict = load_dict(options.dict_file)
-    train_dataset = list(load_data(options.train_data, word_dict))
-    if options.test_data:
-        test_dataset = list(load_data(options.test_data, word_dict))
-    else:
-        test_dataset = None
-
-    trainer_config = parse_config(options.config,
-                                  "dict_file=%s" % options.dict_file)
-    # No need to have data provider for trainer
-    trainer_config.ClearField('data_config')
-    trainer_config.ClearField('test_data_config')
-
-    # create a GradientMachine from the model configuratin
-    model = api.GradientMachine.createFromConfigProto(
-        trainer_config.model_config)
-    # create a trainer for the gradient machine
-    trainer = api.Trainer.create(trainer_config, model)
-
-    # create a data converter which converts data to PaddlePaddle
-    # internal format
-    input_types = [
-        integer_value_sequence(len(word_dict)) if options.seq else
-        sparse_binary_vector(len(word_dict)), integer_value(2)
-    ]
-    converter = DataProviderConverter(input_types)
-
-    batch_size = trainer_config.opt_config.batch_size
-    trainer.startTrain()
-    for train_pass in xrange(options.num_passes):
-        trainer.startTrainPass()
-        random.shuffle(train_dataset)
-        for pos in xrange(0, len(train_dataset), batch_size):
-            batch = itertools.islice(train_dataset, pos, pos + batch_size)
-            size = min(batch_size, len(train_dataset) - pos)
-            trainer.trainOneDataBatch(size, converter(batch))
-        trainer.finishTrainPass()
-        if test_dataset:
-            trainer.startTestPeriod()
-            for pos in xrange(0, len(test_dataset), batch_size):
-                batch = itertools.islice(test_dataset, pos, pos + batch_size)
-                size = min(batch_size, len(test_dataset) - pos)
-                trainer.testOneDataBatch(size, converter(batch))
-            trainer.finishTestPeriod()
-    trainer.finishTrain()
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/quick_start/api_train.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-# Note: if using trainer_config.emb.py, trainer_config.cnn.py
-# or trainer_config.lstm.py, you need to change --seq to --seq=1
-# because they are sequence models.
-python api_train.py \
-  --config=trainer_config.lr.py \
-  --trainer_count=2 \
-  --num_passes=15 \
-  --use_gpu=0 \
-  --seq=0 \
-  --train_data=data/train.txt \
-  --test_data=data/test.txt \
-  --dict_file=data/dict.txt \
-  2>&1 | tee 'train.log'

v1_api_demo/quick_start/cluster/cluster_train.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-# Should run pserver.sh before run this script.
-bin_dir=$(cd `dirname $0`; pwd)
-home_dir=$(cd "${bin_dir}/.."; pwd)
-source "$bin_dir/env.sh"
-
-model_dir="$bin_dir/output"
-log_file="$bin_dir/train.log"
-
-pushd "$home_dir"
-cfg=trainer_config.lr.py
-paddle train \
-  --start_pserver=false \
-  --config=$cfg \
-  --save_dir=${model_dir} \
-  --trainer_count=4 \
-  --local=0 \
-  --log_period=100 \
-  --num_passes=15 \
-  --use_gpu=false \
-  --show_parameter_stats_period=100 \
-  --test_all_data_in_one_period=1 \
-  --num_gradient_servers=1 \
-  --nics=`get_nics` \
-  --port=7164 \
-  --ports_num=1 \
-  --pservers="127.0.0.1" \
-  --comment="paddle_trainer" \
-  2>&1 | tee "$log_file"
-popd

v1_api_demo/quick_start/cluster/env.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-function get_nics() {
-  machine=`uname -s`
-  local nics=""
-  if [ "$machine" == "Linux" ]; then
-    nics="lo"
-  elif [ "$machine" == "Darwin" ]; then
-    nics="lo0"
-  else
-    nics="unsupport"
-  fi
-  echo $nics
-}

v1_api_demo/quick_start/cluster/pserver.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-bin_dir=$(cd `dirname $0`; pwd)
-source "$bin_dir/env.sh"
-
-paddle pserver \
-  --nics=`get_nics` \
-  --port=7164 \
-  --ports_num=1 \
-  --ports_num_for_sparse=1 \
-  --num_gradient_servers=1 \
-  --comment="paddle_pserver" \
-  2>&1 | tee 'pserver.log'

v1_api_demo/quick_start/data/README.md

-This dataset consists of electronics product reviews associated with
-binary labels (positive/negative) for sentiment classification.
-
-The preprocessed data can be downloaded by script `get_data.sh`.
-The data was derived from reviews_Electronics_5.json.gz at
-
-http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
-
-If you want to process the raw data, you can use the script `proc_from_raw_data/get_data.sh`.

v1_api_demo/quick_start/data/get_data.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-# Download the preprocessed data
-wget http://paddlepaddle.bj.bcebos.com/demo/quick_start_preprocessed_data/preprocessed_data.tar.gz
-
-# Extract package
-tar zxvf preprocessed_data.tar.gz
-
-# Remove compressed package
-rm preprocessed_data.tar.gz

v1_api_demo/quick_start/data/proc_from_raw_data/get_data.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# 1. size of pos : neg = 1:1.
-# 2. size of testing set = min(25k, len(all_data) * 0.1), others is traning set.
-# 3. distinct train set and test set.
-
-set -e
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-# Download data
-echo "Downloading Amazon Electronics reviews data..."
-# http://jmcauley.ucsd.edu/data/amazon/
-wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
-echo "Downloading mosesdecoder..."
-# https://github.com/moses-smt/mosesdecoder
-wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
-
-unzip master.zip
-rm master.zip
-
-##################
-# Preprocess data 
-echo "Preprocess data..."
-export LC_ALL=C
-UNAME_STR=`uname`
-
-if [ ${UNAME_STR} == 'Linux' ]; then
-  SHUF_PROG='shuf'
-else
-  SHUF_PROG='gshuf'
-fi
-
-mkdir -p tmp
-python preprocess.py -i reviews_Electronics_5.json.gz
-# uniq and shuffle
-cd tmp
-echo 'Uniq and shuffle...'
-cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed
-cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed
-
-min_len=`sed -n '$=' neg.shuffed`
-test_num=$((min_len/10))
-if [ $test_num -gt 12500 ];then
- test_num=12500
-fi
-train_num=$((min_len-test_num))
-
-head -n$train_num pos.shuffed >train.pos
-head -n$train_num neg.shuffed >train.neg
-tail -n$test_num pos.shuffed >test.pos
-tail -n$test_num neg.shuffed >test.neg
-
-cat train.pos train.neg | ${SHUF_PROG} >../train.txt
-cat test.pos test.neg | ${SHUF_PROG} >../test.txt
-
-cd -
-echo 'train.txt' > train.list
-echo 'test.txt' > test.list
-
-# use 30k dict
-rm -rf tmp
-mv dict.txt dict_all.txt
-cat dict_all.txt | head -n 30001 > dict.txt
-echo 'Done.'

v1_api_demo/quick_start/data/proc_from_raw_data/preprocess.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# -*- coding: UTF-8 -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-1. Tokenize the words and punctuation 
-2. pos sample : rating score 5; neg sample: rating score 1-2.
-
-Usage:
-    python preprocess.py -i data_file [random seed]
-"""
-
-import sys
-import os
-import operator
-import gzip
-from subprocess import Popen, PIPE
-from optparse import OptionParser
-import json
-from multiprocessing import Queue
-from multiprocessing import Pool
-import multiprocessing
-
-batch_size = 5000
-word_count = {}
-num_tokenize = max(1,
-                   multiprocessing.cpu_count() - 2)  # parse + tokenize + save
-max_queue_size = 8
-parse_queue = Queue(maxsize=max_queue_size + num_tokenize)
-tokenize_queue = Queue(maxsize=max_queue_size + num_tokenize)
-
-
-def create_dict(data):
-    """
-    Create dictionary based on data, and saved in data_dir/dict.txt.
-    The first line is unk \t -1.
-    data: list, input data by batch.
-    """
-    for seq in data:
-        try:
-            for w in seq.lower().split():
-                if w not in word_count:
-                    word_count[w] = 1
-                else:
-                    word_count[w] += 1
-        except:
-            sys.stderr.write(seq + "\tERROR\n")
-
-
-def parse(path):
-    """
-    Open .gz file.
-    """
-    sys.stderr.write(path)
-    g = gzip.open(path, 'r')
-    for l in g:
-        yield json.loads(l)
-    g.close()
-
-
-def tokenize(sentences):
-    """
-    Use tokenizer.perl to tokenize input sentences.
-    tokenizer.perl is tool of Moses.
-    sentences : a list of input sentences.
-    return: a list of processed text.
-    """
-    dir = './mosesdecoder-master/scripts/tokenizer/tokenizer.perl'
-    if not os.path.exists(dir):
-        sys.exit(
-            "The ./mosesdecoder-master/scripts/tokenizer/tokenizer.perl does not exists."
-        )
-    tokenizer_cmd = [dir, '-l', 'en', '-q', '-']
-    assert isinstance(sentences, list)
-    text = "\n".join(sentences)
-    tokenizer = Popen(tokenizer_cmd, stdin=PIPE, stdout=PIPE)
-    tok_text, _ = tokenizer.communicate(text)
-    toks = tok_text.split('\n')[:-1]
-    return toks
-
-
-def save_data(instance, data_dir, pre_fix, batch_num):
-    """
-    save data by batch
-    """
-    label = ['1' if pre_fix == 'pos' else '0' for i in range(len(instance))]
-    lines = ['%s\t%s' % (label[i], instance[i]) for i in range(len(label))]
-    file_name = os.path.join(data_dir, "%s_%s.txt" % (pre_fix, batch_num))
-    file(file_name, 'w').write('\n'.join(lines) + '\n')
-
-
-def tokenize_batch(id):
-    """
-    tokenize data by batch
-    """
-    while True:
-        num_batch, instance, pre_fix = parse_queue.get()
-        if num_batch == -1:  ### parse_queue finished
-            tokenize_queue.put((-1, None, None))
-            sys.stderr.write("Thread %s finish\n" % (id))
-            break
-        tokenize_instance = tokenize(instance)
-        tokenize_queue.put((num_batch, tokenize_instance, pre_fix))
-        sys.stderr.write('.')
-
-
-def save_batch(data_dir, num_tokenize, data_dir_dict):
-    """
-        save data by batch
-        build dict.txt
-    """
-    token_count = 0
-    while True:
-        num_batch, instance, pre_fix = tokenize_queue.get()
-        if num_batch == -1:
-            token_count += 1
-            if token_count == num_tokenize:  #### tokenize finished.
-                break
-            else:
-                continue
-        save_data(instance, data_dir, pre_fix, num_batch)
-        create_dict(instance)  ## update dict
-
-    sys.stderr.write("save file finish\n")
-    f = open(data_dir_dict, 'w')
-    f.write('%s\t%s\n' % ('unk', '-1'))
-    for k, v in sorted(word_count.items(), key=operator.itemgetter(1), \
-                       reverse=True):
-        f.write('%s\t%s\n' % (k, v))
-    f.close()
-    sys.stderr.write("build dict finish\n")
-
-
-def parse_batch(data, num_tokenize):
-    """
-    parse data by batch
-    parse -> tokenize -> save
-    """
-    raw_txt = parse(data)
-    neg, pos = [], []
-    count = 0
-    sys.stderr.write("extract raw data\n")
-    for l in raw_txt:
-        rating = l["overall"]
-        text = l["reviewText"].lower()  # # convert words to lower case
-        if rating == 5.0 and text:
-            pos.append(text)
-        if rating < 3.0 and text:
-            neg.append(text)
-        if len(pos) == batch_size or len(neg) == batch_size:
-            if len(pos) == batch_size:
-                batch = pos
-                pre_fix = 'pos'
-            else:
-                batch = neg
-                pre_fix = 'neg'
-
-            parse_queue.put((count, batch, pre_fix))
-            count += 1
-            if pre_fix == 'pos':
-                pos = []
-            else:
-                neg = []
-
-    if len(pos) > 0:
-        parse_queue.put((count, pos, 'pos'))
-        count += 1
-    if len(neg) > 0:
-        parse_queue.put((count, neg, 'neg'))
-        count += 1
-    for i in range(num_tokenize):
-        parse_queue.put((-1, None, None))  #### for tokenize's input finished
-    sys.stderr.write("parsing finish\n")
-
-
-def option_parser():
-    parser = OptionParser(usage="usage: python preprcoess.py "\
-                                "-i data_path [options]")
-    parser.add_option(
-        "-i", "--data", action="store", dest="input", help="Input data path.")
-    parser.add_option(
-        "-s",
-        "--seed",
-        action="store",
-        dest="seed",
-        default=1024,
-        help="Set random seed.")
-    return parser.parse_args()
-
-
-def main():
-    reload(sys)
-    sys.setdefaultencoding('utf-8')
-    options, args = option_parser()
-    data = options.input
-    seed = options.seed
-    data_dir_dict = os.path.join(os.path.dirname(data), 'dict.txt')
-    data_dir = os.path.join(os.path.dirname(data), 'tmp')
-    pool = Pool(processes=num_tokenize + 2)
-    pool.apply_async(parse_batch, args=(data, num_tokenize))
-    for i in range(num_tokenize):
-        pool.apply_async(tokenize_batch, args=(str(i), ))
-    pool.apply_async(save_batch, args=(data_dir, num_tokenize, data_dir_dict))
-    pool.close()
-    pool.join()
-
-    file(os.path.join(os.path.dirname(data), 'labels.list'),
-         'w').write('neg\t0\npos\t1\n')
-
-
-if __name__ == '__main__':
-    main()

v1_api_demo/quick_start/dataprovider_bow.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer.PyDataProvider2 import *
-
-# id of the word not in dictionary
-UNK_IDX = 0
-
-
-# initializer is called by the framework during initialization.
-# It allows the user to describe the data types and setup the
-# necessary data structure for later use.
-# `settings` is an object. initializer need to properly fill settings.input_types.
-# initializer can also store other data structures needed to be used at process().
-# In this example, dictionary is stored in settings.
-# `dictionay` and `kwargs` are arguments passed from trainer_config.lr.py
-def initializer(settings, dictionary, **kwargs):
-    # Put the word dictionary into settings
-    settings.word_dict = dictionary
-
-    # setting.input_types specifies what the data types the data provider
-    # generates.
-    settings.input_types = {
-        # The first input is a sparse_binary_vector,
-        # which means each dimension of the vector is either 0 or 1. It is the
-        # bag-of-words (BOW) representation of the texts.
-        'word': sparse_binary_vector(len(dictionary)),
-        # The second input is an integer. It represents the category id of the
-        # sample. 2 means there are two labels in the dataset.
-        # (1 for positive and 0 for negative)
-        'label': integer_value(2)
-    }
-
-
-# Delaring a data provider. It has an initializer 'data_initialzer'.
-# It will cache the generated data of the first pass in memory, so that
-# during later pass, no on-the-fly data generation will be needed.
-# `setting` is the same object used by initializer()
-# `file_name` is the name of a file listed train_list or test_list file given
-# to define_py_data_sources2(). See trainer_config.lr.py.
-@provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_name):
-    # Open the input data file.
-    with open(file_name, 'r') as f:
-        # Read each line.
-        for line in f:
-            # Each line contains the label and text of the comment, separated by \t.
-            label, comment = line.strip().split('\t')
-
-            # Split the words into a list.
-            words = comment.split()
-
-            # convert the words into a list of ids by looking them up in word_dict.
-            word_vector = [settings.word_dict.get(w, UNK_IDX) for w in words]
-
-            # Return the features for the current comment. The first is a list
-            # of ids representing a 0-1 binary sparse vector of the text,
-            # the second is the integer id of the label.
-            yield {'word': word_vector, 'label': int(label)}
-
-
-def predict_initializer(settings, dictionary, **kwargs):
-    settings.word_dict = dictionary
-    settings.input_types = {'word': sparse_binary_vector(len(dictionary))}
-
-
-# Declaring a data provider for prediction. The difference with process
-# is that label is not generated.
-@provider(init_hook=predict_initializer, should_shuffle=False)
-def process_predict(settings, file_name):
-    with open(file_name, 'r') as f:
-        for line in f:
-            comment = line.strip().split()
-            word_vector = [settings.word_dict.get(w, UNK_IDX) for w in comment]
-            yield {'word': word_vector}

v1_api_demo/quick_start/dataprovider_emb.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer.PyDataProvider2 import *
-
-UNK_IDX = 0
-
-
-def initializer(settings, dictionary, **kwargs):
-    settings.word_dict = dictionary
-    settings.input_types = {
-        # Define the type of the first input as sequence of integer.
-        # The value of the integers range from 0 to len(dictrionary)-1
-        'word': integer_value_sequence(len(dictionary)),
-        # Define the second input for label id
-        'label': integer_value(2)
-    }
-
-
-@provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_name):
-    with open(file_name, 'r') as f:
-        for line in f:
-            label, comment = line.strip().split('\t')
-            words = comment.split()
-            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
-            yield {'word': word_slot, 'label': int(label)}
-
-
-def predict_initializer(settings, dictionary, **kwargs):
-    settings.word_dict = dictionary
-    settings.input_types = {'word': integer_value_sequence(len(dictionary))}
-
-
-@provider(init_hook=predict_initializer, should_shuffle=False)
-def process_predict(settings, file_name):
-    with open(file_name, 'r') as f:
-        for line in f:
-            comment = line.strip().split()
-            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in comment]
-            yield {'word': word_slot}

v1_api_demo/quick_start/predict.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-cfg=trainer_config.lr.py
-#cfg=trainer_config.emb.py
-#cfg=trainer_config.cnn.py
-#cfg=trainer_config.lstm.py
-model="output/pass-00003"
-paddle train \
-    --config=$cfg \
-    --use_gpu=false \
-    --job=test \
-    --init_model_path=$model \
-    --config_args=is_predict=1 \
-    --predict_output_dir=. \
-2>&1 | tee 'predict.log'
-paddle usage -l 'predict.log' -e $? -n "quick_start_predict_${cfg}" >/dev/null 2>&1
-
-mv rank-00000 result.txt

v1_api_demo/quick_start/train.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-cfg=trainer_config.lr.py
-#cfg=trainer_config.emb.py
-#cfg=trainer_config.cnn.py
-#cfg=trainer_config.lstm.py
-#cfg=trainer_config.bidi-lstm.py
-#cfg=trainer_config.db-lstm.py
-#cfg=trainer_config.resnet-lstm.py
-paddle train \
-  --config=$cfg \
-  --save_dir=./output \
-  --trainer_count=4 \
-  --log_period=100 \
-  --num_passes=15 \
-  --use_gpu=false \
-  --show_parameter_stats_period=100 \
-  --test_all_data_in_one_period=1 \
-  2>&1 | tee 'train.log'
-paddle usage -l "train.log" -e $? -n "quick_start_${cfg}" >/dev/null 2>&1

v1_api_demo/quick_start/trainer_config.bidi-lstm.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
-data = data_layer(name="word", size=len(word_dict))
-emb = embedding_layer(input=data, size=128)
-
-bi_lstm = bidirectional_lstm(input=emb, size=128)
-dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
-
-output = fc_layer(
-    input=dropout, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
-
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/quick_start/trainer_config.cnn.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-data = data_layer(name="word", size=len(word_dict))
-embedding = embedding_layer(input=data, size=128)
-conv = sequence_conv_pool(input=embedding, context_len=3, hidden_size=512)
-output = fc_layer(input=conv, size=2, act=SoftmaxActivation())
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/quick_start/trainer_config.db-lstm.py

-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
-
-data = data_layer(name="word", size=len(word_dict))
-emb = embedding_layer(input=data, size=128)
-
-hidden_0 = mixed_layer(size=128, input=[full_matrix_projection(input=emb)])
-lstm_0 = lstmemory(input=hidden_0, layer_attr=ExtraAttr(drop_rate=0.1))
-
-input_layers = [hidden_0, lstm_0]
-
-for i in range(1, 8):
-    fc = fc_layer(input=input_layers, size=128)
-    lstm = lstmemory(
-        input=fc,
-        layer_attr=ExtraAttr(drop_rate=0.1),
-        reverse=(i % 2) == 1, )
-    input_layers = [fc, lstm]
-
-lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
-
-output = fc_layer(
-    input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
-
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/quick_start/trainer_config.emb.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size, learning_rate=2e-3, learning_method=AdamOptimizer())
-
-data = data_layer(name="word", size=len(word_dict))
-embedding = embedding_layer(input=data, size=128)
-avg = pooling_layer(input=embedding, pooling_type=AvgPooling())
-output = fc_layer(input=avg, size=2, act=SoftmaxActivation())
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/quick_start/trainer_config.lr.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = get_config_arg('dict_file', str, "./data/dict.txt")
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-
-# define the data sources for the model.
-# We need to use different process for training and prediction.
-# For training, the input data includes both word IDs and labels.
-# For prediction, the input data only includs word Ids.
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_bow",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-# Define the data for text features. The size of the data layer is the number
-# of words in the dictionary.
-data = data_layer(name="word", size=len(word_dict))
-
-# Define a fully connected layer with logistic activation.
-# (also called softmax activation).
-output = fc_layer(input=data, size=2, act=SoftmaxActivation())
-
-if not is_predict:
-    # For training, we need label and cost
-
-    # define the category id for each example.
-    # The size of the data layer is the number of labels.
-    label = data_layer(name="label", size=2)
-
-    # Define cross-entropy classification loss and error.
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)
-else:
-    # For prediction, no label is needed. We need to output
-    # We need to output classification result, and class probabilities.
-    maxid = maxid_layer(output)
-    outputs([maxid, output])

v1_api_demo/quick_start/trainer_config.lstm.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-data = data_layer(name="word", size=len(word_dict))
-emb = embedding_layer(input=data, size=128)
-lstm = simple_lstm(
-    input=emb, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.25))
-lstm_max = pooling_layer(input=lstm, pooling_type=MaxPooling())
-output = fc_layer(input=lstm_max, size=2, act=SoftmaxActivation())
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/quick_start/trainer_config.resnet-lstm.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-# edit-mode: -*- python -*-
-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This configuration is a demonstration of how to implement the stacked LSTM
-with residual connections, i.e. an LSTM layer takes the sum of the hidden states
-and inputs of the previous LSTM layer instead of only the hidden states.
-This architecture is from:
-Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi,
-Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey,
-Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser,
-Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens,
-George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa,
-Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. 2016.
-Google's Neural Machine Translation System: Bridging the Gap between Human and
-Machine Translation. In arXiv https://arxiv.org/pdf/1609.08144v2.pdf
-Different from the architecture described in the paper, we use a stack single
-direction LSTM layers as the first layer instead of bi-directional LSTM. Also,
-since this is a demo code, to reduce computation time, we stacked 4 layers
-instead of 8 layers.
-"""
-
-from paddle.trainer_config_helpers import *
-
-dict_file = "./data/dict.txt"
-word_dict = dict()
-with open(dict_file, 'r') as f:
-    for i, line in enumerate(f):
-        w = line.strip().split()[0]
-        word_dict[w] = i
-
-is_predict = get_config_arg('is_predict', bool, False)
-trn = 'data/train.list' if not is_predict else None
-tst = 'data/test.list' if not is_predict else 'data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn,
-    test_list=tst,
-    module="dataprovider_emb",
-    obj=process,
-    args={"dictionary": word_dict})
-
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
-
-data = data_layer(name="word", size=len(word_dict))
-emb = embedding_layer(input=data, size=128)
-lstm = simple_lstm(input=emb, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
-
-previous_input, previous_hidden_state = emb, lstm
-
-for i in range(3):
-    # The input to the current layer is the sum of the hidden state
-    # and input of the previous layer.
-    current_input = addto_layer(input=[previous_input, previous_hidden_state])
-    hidden_state = simple_lstm(
-        input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
-    previous_input, previous_hidden_state = current_input, hidden_state
-
-lstm = previous_hidden_state
-
-lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
-output = fc_layer(
-    input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
-
-if is_predict:
-    maxid = maxid_layer(output)
-    outputs([maxid, output])
-else:
-    label = data_layer(name="label", size=2)
-    cls = classification_cost(input=output, label=label)
-    outputs(cls)

v1_api_demo/sequence_tagging/data/get_data.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-wget http://www.cnts.ua.ac.be/conll2000/chunking/train.txt.gz
-wget http://www.cnts.ua.ac.be/conll2000/chunking/test.txt.gz

v1_api_demo/sequence_tagging/data/test.list

-data/test.txt.gz

v1_api_demo/sequence_tagging/data/train.list

-data/train.txt.gz

v1_api_demo/sequence_tagging/dataprovider.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer.PyDataProvider2 import *
-import gzip
-import logging
-
-logging.basicConfig(
-    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', )
-logger = logging.getLogger('paddle')
-logger.setLevel(logging.INFO)
-
-OOV_POLICY_IGNORE = 0
-OOV_POLICY_USE = 1
-OOV_POLICY_ERROR = 2
-
-num_original_columns = 3
-
-# Feature combination patterns.
-# [[-1,0], [0,0]]  means previous token at column 0 and current token at 
-# column 0 are combined as one feature.
-patterns = [
-    [[-2, 0]],
-    [[-1, 0]],
-    [[0, 0]],
-    [[1, 0]],
-    [[2, 0]],
-    [[-1, 0], [0, 0]],
-    [[0, 0], [1, 0]],
-    [[-2, 1]],
-    [[-1, 1]],
-    [[0, 1]],
-    [[1, 1]],
-    [[2, 1]],
-    [[-2, 1], [-1, 1]],
-    [[-1, 1], [0, 1]],
-    [[0, 1], [1, 1]],
-    [[1, 1], [2, 1]],
-    [[-2, 1], [-1, 1], [0, 1]],
-    [[-1, 1], [0, 1], [1, 1]],
-    [[0, 1], [1, 1], [2, 1]],
-]
-
-dict_label = {
-    'B-ADJP': 0,
-    'I-ADJP': 1,
-    'B-ADVP': 2,
-    'I-ADVP': 3,
-    'B-CONJP': 4,
-    'I-CONJP': 5,
-    'B-INTJ': 6,
-    'I-INTJ': 7,
-    'B-LST': 8,
-    'I-LST': 9,
-    'B-NP': 10,
-    'I-NP': 11,
-    'B-PP': 12,
-    'I-PP': 13,
-    'B-PRT': 14,
-    'I-PRT': 15,
-    'B-SBAR': 16,
-    'I-SBAR': 17,
-    'B-UCP': 18,
-    'I-UCP': 19,
-    'B-VP': 20,
-    'I-VP': 21,
-    'O': 22
-}
-
-
-def make_features(sequence):
-    length = len(sequence)
-    num_features = len(sequence[0])
-
-    def get_features(pos):
-        if pos < 0:
-            return ['#B%s' % -pos] * num_features
-        if pos >= length:
-            return ['#E%s' % (pos - length + 1)] * num_features
-        return sequence[pos]
-
-    for i in xrange(length):
-        for pattern in patterns:
-            fname = '/'.join([get_features(i + pos)[f] for pos, f in pattern])
-            sequence[i].append(fname)
-
-
-'''
-Source file format:
-Each line is for one timestep. The features are separated by space.
-An empty line indicates end of a sequence.
-
-cutoff: a list of numbers. If count of a feature is smaller than this,
- it will be ignored.
-if oov_policy[i] is OOV_POLICY_USE, id 0 is reserved for OOV features of
-i-th column.
-
-return a list of dict for each column
-'''
-
-
-def create_dictionaries(filename, cutoff, oov_policy):
-    def add_to_dict(sequence, dicts):
-        num_features = len(dicts)
-        for features in sequence:
-            l = len(features)
-            assert l == num_features, "Wrong number of features " + line
-            for i in xrange(l):
-                if features[i] in dicts[i]:
-                    dicts[i][features[i]] += 1
-                else:
-                    dicts[i][features[i]] = 1
-
-    num_features = len(cutoff)
-    dicts = []
-    for i in xrange(num_features):
-        dicts.append(dict())
-
-    f = gzip.open(filename, 'rb')
-
-    sequence = []
-
-    for line in f:
-        line = line.strip()
-        if not line:
-            make_features(sequence)
-            add_to_dict(sequence, dicts)
-            sequence = []
-            continue
-        features = line.split(' ')
-        sequence.append(features)
-
-    for i in xrange(num_features):
-        dct = dicts[i]
-        n = 1 if oov_policy[i] == OOV_POLICY_USE else 0
-        todo = []
-        for k, v in dct.iteritems():
-            if v < cutoff[i]:
-                todo.append(k)
-            else:
-                dct[k] = n
-                n += 1
-
-        if oov_policy[i] == OOV_POLICY_USE:
-            # placeholder so that len(dct) will be the number of features
-            # including OOV
-            dct['#OOV#'] = 0
-
-        logger.info('column %d dict size=%d, ignored %d' % (i, n, len(todo)))
-        for k in todo:
-            del dct[k]
-
-    f.close()
-    return dicts
-
-
-def initializer(settings, **xargs):
-    cutoff = [3, 1, 0]
-    cutoff += [3] * len(patterns)
-    oov_policy = [OOV_POLICY_IGNORE, OOV_POLICY_ERROR, OOV_POLICY_ERROR]
-    oov_policy += [OOV_POLICY_IGNORE] * len(patterns)
-    dicts = create_dictionaries('data/train.txt.gz', cutoff, oov_policy)
-    dicts[2] = dict_label
-    settings.dicts = dicts
-    settings.oov_policy = oov_policy
-    input_types = []
-    num_features = len(dicts)
-    for i in xrange(num_original_columns):
-        input_types.append(integer_sequence(len(dicts[i])))
-        logger.info("slot %s size=%s" % (i, len(dicts[i])))
-    if patterns:
-        dim = 0
-        for i in xrange(num_original_columns, num_features):
-            dim += len(dicts[i])
-        input_types.append(sparse_binary_vector_sequence(dim))
-        logger.info("feature size=%s" % dim)
-    settings.input_types = input_types
-
-
-'''
-if oov_policy[i] == OOV_POLICY_USE, features in i-th column which are not
-existed in dicts[i] will be assigned to id 0.
-if oov_policy[i] == OOV_POLICY_ERROR, all features in i-th column MUST exist
-in dicts[i].
-'''
-
-
-@provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, filename):
-    input_file = filename
-    dicts = settings.dicts
-    oov_policy = settings.oov_policy
-
-    def gen_sample(sequence):
-        num_features = len(dicts)
-        sample = [list() for i in xrange(num_original_columns)]
-        if patterns:
-            sample.append([])
-        for features in sequence:
-            assert len(features) == num_features, \
-                "Wrong number of features: " + line
-            for i in xrange(num_original_columns):
-                id = dicts[i].get(features[i], -1)
-                if id != -1:
-                    sample[i].append(id)
-                elif oov_policy[i] == OOV_POLICY_IGNORE:
-                    sample[i].append(0xffffffff)
-                elif oov_policy[i] == OOV_POLICY_ERROR:
-                    logger.fatal("Unknown token: %s" % features[i])
-                else:
-                    sample[i].append(0)
-
-            if patterns:
-                dim = 0
-                vec = []
-                for i in xrange(num_original_columns, num_features):
-                    id = dicts[i].get(features[i], -1)
-                    if id != -1:
-                        vec.append(dim + id)
-                    elif oov_policy[i] == OOV_POLICY_IGNORE:
-                        pass
-                    elif oov_policy[i] == OOV_POLICY_ERROR:
-                        logger.fatal("Unknown token: %s" % features[i])
-                    else:
-                        vec.ids.append(dim + 0)
-
-                    dim += len(dicts[i])
-                sample[-1].append(vec)
-        return sample
-
-    num_features = len(dicts)
-    f = gzip.open(input_file, 'rb')
-
-    num_sequences = 0
-    sequence = []
-    for line in f:
-        line = line.strip()
-        if not line:
-            make_features(sequence)
-            yield gen_sample(sequence)
-            sequence = []
-            num_sequences += 1
-            continue
-        features = line.split(' ')
-        sequence.append(features)
-
-    f.close()
-
-    logger.info("num_sequences=%s" % num_sequences)

v1_api_demo/sequence_tagging/linear_crf.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-import math
-
-define_py_data_sources2(
-    train_list="data/train.list",
-    test_list="data/test.list",
-    module="dataprovider",
-    obj="process")
-
-batch_size = 1
-settings(
-    learning_method=MomentumOptimizer(),
-    batch_size=batch_size,
-    regularization=L2Regularization(batch_size * 1e-4),
-    model_average=ModelAverage(0.5),
-    learning_rate=1e-1,
-    learning_rate_decay_a=1e-5,
-    learning_rate_decay_b=0.25, )
-
-num_label_types = 23
-
-
-def get_simd_size(size):
-    return int(math.ceil(float(size) / 8)) * 8
-
-
-# Currently, in order to use sparse_update=True,
-# the size has to be aligned.
-num_label_types = get_simd_size(num_label_types)
-
-features = data_layer(name="features", size=76328)
-word = data_layer(name="word", size=6778)
-pos = data_layer(name="pos", size=44)
-chunk = data_layer(name="chunk", size=num_label_types)
-
-crf_input = fc_layer(
-    input=features,
-    size=num_label_types,
-    act=LinearActivation(),
-    bias_attr=False,
-    param_attr=ParamAttr(
-        initial_std=0, sparse_update=True))
-
-crf = crf_layer(
-    input=crf_input,
-    label=chunk,
-    param_attr=ParamAttr(
-        name="crfw", initial_std=0), )
-
-crf_decoding = crf_decoding_layer(
-    size=num_label_types,
-    input=crf_input,
-    label=chunk,
-    param_attr=ParamAttr(name="crfw"), )
-
-sum_evaluator(
-    name="error",
-    input=crf_decoding, )
-
-chunk_evaluator(
-    name="chunk_f1",
-    input=crf_decoding,
-    label=chunk,
-    chunk_scheme="IOB",
-    num_chunk_types=11, )
-
-inputs(word, pos, chunk, features)
-outputs(crf)

v1_api_demo/sequence_tagging/readme.md

-# Sequence Tagging
-
-This demo is a sequence model for assigning tags to each token in a sentence. The task is described at <a href = "http://www.cnts.ua.ac.be/conll2000/chunking">CONLL2000 Text Chunking</a> task.
-
-## Download data
-```bash
-cd demo/sequence_tagging
-./data/get_data.sh
-```
-
-## Train model
-```bash
-cd demo/sequence_tagging
-./train.sh
-```
-
-## Model description
-
-We provide two models. One is a linear CRF model (linear_crf.py) with is equivalent to the one at <a href="http://leon.bottou.org/projects/sgd#stochastic_gradient_crfs">leon.bottou.org/projects/sgd</a>. The second one is a stacked bidirectional RNN and CRF model (rnn_crf.py).
-<center>
-<table border="2" cellspacing="0" cellpadding="6" rules="all" frame="border">
-
-<thead>
-<th scope="col" class="left">Model name</th>
-<th scope="col" class="left">Number of parameters</th>
-<th scope="col" class="left">F1 score</th>
-</thead>
-
-<tbody>
-<tr>
-<td class="left">linear_crf</td>
-<td class="left"> 1.8M </td>
-<td class="left"> 0.937</td>
-</tr>
-
-<tr>
-<td class="left">rnn_crf</td>
-<td class="left"> 960K </td>
-<td class="left">0.941</td>
-</tr>
-
-</tbody>
-</table>
-</center>
-<br>

v1_api_demo/sequence_tagging/rnn_crf.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-import math
-
-define_py_data_sources2(
-    train_list="data/train.list",
-    test_list="data/test.list",
-    module="dataprovider",
-    obj="process")
-
-batch_size = 16
-settings(
-    learning_method=MomentumOptimizer(),
-    batch_size=batch_size,
-    regularization=L2Regularization(batch_size * 1e-5),
-    model_average=ModelAverage(0.5),
-    learning_rate=2e-3,
-    learning_rate_decay_a=5e-7,
-    learning_rate_decay_b=0.5, )
-
-word_dim = 128
-hidden_dim = 128
-with_rnn = True
-
-initial_std = 1 / math.sqrt(hidden_dim)
-param_attr = ParamAttr(initial_std=initial_std)
-cpu_layer_attr = ExtraLayerAttribute(device=-1)
-
-default_device(0)
-
-num_label_types = 23
-
-features = data_layer(name="features", size=76328)
-word = data_layer(name="word", size=6778)
-pos = data_layer(name="pos", size=44)
-chunk = data_layer(
-    name="chunk", size=num_label_types, layer_attr=cpu_layer_attr)
-
-emb = embedding_layer(
-    input=word, size=word_dim, param_attr=ParamAttr(initial_std=0))
-
-hidden1 = mixed_layer(
-    size=hidden_dim,
-    act=STanhActivation(),
-    bias_attr=True,
-    input=[
-        full_matrix_projection(emb), table_projection(
-            pos, param_attr=param_attr)
-    ])
-
-if with_rnn:
-    rnn1 = recurrent_layer(
-        act=ReluActivation(),
-        bias_attr=True,
-        input=hidden1,
-        param_attr=ParamAttr(initial_std=0), )
-
-hidden2 = mixed_layer(
-    size=hidden_dim,
-    act=STanhActivation(),
-    bias_attr=True,
-    input=[full_matrix_projection(hidden1)] +
-    ([full_matrix_projection(
-        rnn1, param_attr=ParamAttr(initial_std=0))] if with_rnn else []), )
-
-if with_rnn:
-    rnn2 = recurrent_layer(
-        reverse=True,
-        act=ReluActivation(),
-        bias_attr=True,
-        input=hidden2,
-        param_attr=ParamAttr(initial_std=0), )
-
-crf_input = mixed_layer(
-    size=num_label_types,
-    bias_attr=False,
-    input=[full_matrix_projection(hidden2), ] +
-    ([full_matrix_projection(
-        rnn2, param_attr=ParamAttr(initial_std=0))] if with_rnn else []), )
-
-crf = crf_layer(
-    input=crf_input,
-    label=chunk,
-    param_attr=ParamAttr(
-        name="crfw", initial_std=0),
-    layer_attr=cpu_layer_attr, )
-
-crf_decoding = crf_decoding_layer(
-    size=num_label_types,
-    input=crf_input,
-    label=chunk,
-    param_attr=ParamAttr(name="crfw"),
-    layer_attr=cpu_layer_attr, )
-
-sum_evaluator(
-    name="error",
-    input=crf_decoding, )
-
-chunk_evaluator(
-    name="chunk_f1",
-    input=crf_decoding,
-    label=chunk,
-    chunk_scheme="IOB",
-    num_chunk_types=11, )
-
-inputs(word, pos, chunk, features)
-outputs(crf)

v1_api_demo/sequence_tagging/train.sh

-#!/bin/bash
-
-paddle train \
-       --config rnn_crf.py \
-       --parallel_nn=1 \
-       --use_gpu=1 \
-       --dot_period=10 \
-       --log_period=1000 \
-       --test_period=0 \
-       --num_passes=10 \
-2>&1 | tee 'train.log'
-paddle usage -l 'train.log' -e $? -n "sequence_tagging_train" >/dev/null 2>&1

v1_api_demo/sequence_tagging/train_linear.sh

-#!/bin/bash
-
-paddle train \
-       --config linear_crf.py \
-       --use_gpu=0 \
-       --dot_period=100 \
-       --log_period=10000 \
-       --test_period=0 \
-       --num_passes=10
-2>&1 | tee 'train_linear.log'
-paddle usage -l 'train_linear.log' -e $? -n "sequence_tagging_train_linear" >/dev/null 2>&1

v1_api_demo/traffic_prediction/README

-run by:
-cd ./data
-sh get_data.sh
-cd ..
-sh train.sh
-sh predict.sh
-

v1_api_demo/traffic_prediction/data/get_data.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-set -e
-set -x
-
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
-
-#download the dataset
-echo "Downloading traffic data..."
-wget http://paddlepaddle.cdn.bcebos.com/demo/traffic/traffic_data.tar.gz
-
-#extract package
-echo "Unzipping..."
-tar -zxvf traffic_data.tar.gz
-
-echo "data/speeds.csv" > train.list
-echo "data/speeds.csv" > test.list
-echo "data/speeds.csv" > pred.list
-
-echo "Done."

v1_api_demo/traffic_prediction/dataprovider.py

-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer.PyDataProvider2 import *
-import sys
-import numpy as np
-TERM_NUM = 24
-FORECASTING_NUM = 24
-LABEL_VALUE_NUM = 4
-
-
-def initHook(settings, file_list, **kwargs):
-    """
-    Init hook is invoked before process data. It will set obj.slots and store data meta.
-
-    :param settings: global object. It will passed to process routine.
-    :type obj: object
-    :param file_list: the meta file object, which passed from trainer_config.py,but unused in this function.
-    :param kwargs: unused other arguments.
-    """
-    del kwargs  #unused 
-
-    settings.pool_size = sys.maxint
-    #Use a time seires of the past as feature.
-    #Dense_vector's expression form is [float,float,...,float]
-    settings.input_types = [dense_vector(TERM_NUM)]
-    #There are next FORECASTING_NUM fragments you need predict.
-    #Every predicted condition at time point has four states.
-    for i in range(FORECASTING_NUM):
-        settings.input_types.append(integer_value(LABEL_VALUE_NUM))
-
-
-@provider(
-    init_hook=initHook, cache=CacheType.CACHE_PASS_IN_MEM, should_shuffle=True)
-def process(settings, file_name):
-    with open(file_name) as f:
-        #abandon fields name
-        f.next()
-        for row_num, line in enumerate(f):
-            speeds = map(int, line.rstrip('\r\n').split(",")[1:])
-            # Get the max index.
-            end_time = len(speeds)
-            # Scanning and generating samples
-            for i in range(TERM_NUM, end_time - FORECASTING_NUM):
-                # For dense slot
-                pre_spd = map(float, speeds[i - TERM_NUM:i])
-
-                # Integer value need predicting, values start from 0, so every one minus 1.
-                fol_spd = [j - 1 for j in speeds[i:i + FORECASTING_NUM]]
-
-                # Predicting label is missing, abandon the sample.
-                if -1 in fol_spd:
-                    continue
-                yield [pre_spd] + fol_spd
-
-
-def predict_initHook(settings, file_list, **kwargs):
-    settings.pool_size = sys.maxint
-    settings.input_types = [dense_vector(TERM_NUM)]
-
-
-@provider(init_hook=predict_initHook, should_shuffle=False)
-def process_predict(settings, file_name):
-    with open(file_name) as f:
-        #abandon fields name
-        f.next()
-        for row_num, line in enumerate(f):
-            speeds = map(int, line.rstrip('\r\n').split(","))
-            end_time = len(speeds)
-            pre_spd = map(float, speeds[end_time - TERM_NUM:end_time])
-            yield pre_spd

v1_api_demo/traffic_prediction/gen_result.py

-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-res = []
-with open('./rank-00000') as f:
-    for line in f:
-        pred = map(int, line.strip('\r\n;').split(";"))
-        #raw prediction range from 0 to 3
-        res.append([i + 1 for i in pred])
-
-file_name = open('./data/pred.list').read().strip('\r\n')
-
-FORECASTING_NUM = 24
-header = [
-    'id',
-    '201604200805',
-    '201604200810',
-    '201604200815',
-    '201604200820',
-    '201604200825',
-    '201604200830',
-    '201604200835',
-    '201604200840',
-    '201604200845',
-    '201604200850',
-    '201604200855',
-    '201604200900',
-    '201604200905',
-    '201604200910',
-    '201604200915',
-    '201604200920',
-    '201604200925',
-    '201604200930',
-    '201604200935',
-    '201604200940',
-    '201604200945',
-    '201604200950',
-    '201604200955',
-    '201604201000',
-]
-###################
-## To CSV format ##
-###################
-with open(file_name) as f:
-    f.next()
-    print ','.join(header)
-    for row_num, line in enumerate(f):
-        fields = line.rstrip('\r\n').split(',')
-        linkid = fields[0]
-        print linkid + ',' + ','.join(map(str, res[row_num]))

v1_api_demo/traffic_prediction/predict.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-cfg=trainer_config.py
-# pass choice 
-model="output/pass-00000"
-paddle train \
-    --config=$cfg \
-    --use_gpu=false \
-    --job=test \
-    --init_model_path=$model \
-    --config_args=is_predict=1 \
-    --predict_output_dir=. 
-
-python gen_result.py > result.csv
-
-rm -rf rank-00000

v1_api_demo/traffic_prediction/train.sh

-#!/bin/bash
-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-set -e
-
-cfg=trainer_config.py
-paddle train \
-  --config=$cfg \
-  --save_dir=./output \
-  --trainer_count=4 \
-  --log_period=1000 \
-  --dot_period=10 \
-  --num_passes=10 \
-  --use_gpu=false \
-  --show_parameter_stats_period=3000 \
-  2>&1 | tee 'train.log'

v1_api_demo/traffic_prediction/trainer_config.py

-# Copyright (c) 2016 PaddlePaddle Authors, Inc. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from paddle.trainer_config_helpers import *
-
-################################### DATA Configuration #############################################
-is_predict = get_config_arg('is_predict', bool, False)
-trn = './data/train.list' if not is_predict else None
-tst = './data/test.list' if not is_predict else './data/pred.list'
-process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(
-    train_list=trn, test_list=tst, module="dataprovider", obj=process)
-################################### Parameter Configuaration #######################################
-TERM_NUM = 24
-FORECASTING_NUM = 24
-emb_size = 16
-batch_size = 128 if not is_predict else 1
-settings(
-    batch_size=batch_size,
-    learning_rate=1e-3,
-    learning_method=RMSPropOptimizer())
-################################### Algorithm Configuration ########################################
-
-output_label = []
-
-link_encode = data_layer(name='link_encode', size=TERM_NUM)
-for i in xrange(FORECASTING_NUM):
-    # Each task share same weight.
-    link_param = ParamAttr(
-        name='_link_vec.w', initial_max=1.0, initial_min=-1.0)
-    link_vec = fc_layer(input=link_encode, size=emb_size, param_attr=link_param)
-    score = fc_layer(input=link_vec, size=4, act=SoftmaxActivation())
-    if is_predict:
-        maxid = maxid_layer(score)
-        output_label.append(maxid)
-    else:
-        # Multi-task training.
-        label = data_layer(name='label_%dmin' % ((i + 1) * 5), size=4)
-        cls = classification_cost(
-            input=score, name="cost_%dmin" % ((i + 1) * 5), label=label)
-        output_label.append(cls)
-outputs(output_label)

v1_api_demo/vae/README.md

-#Variational Autoencoder (VAE)
-
-This demo implements VAE training described in the original paper (https://arxiv.org/abs/1312.6114).
-
-
-In order to run the model, first download the MNIST dataset by running the shell script in ./data.
-
-Then you can run the command below. The flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).  
-
-$python vae_train.py [--use_gpu 1]
-
-The generated images will be stored in ./samples/
-The corresponding models will be stored in ./params/

v1_api_demo/vae/data/get_mnist_data.sh

-#!/usr/bin/env sh
-# This script downloads the mnist data and unzips it.
-set -e
-DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-rm -rf "$DIR/mnist_data"
-mkdir "$DIR/mnist_data"
-cd "$DIR/mnist_data"
-
-echo "Downloading..."
-
-for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
-do
-    if [ ! -e $fname ]; then
-        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
-        gunzip ${fname}.gz
-    fi
-done

v1_api_demo/vae/dataloader.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-
-
-class MNISTloader():
-    def __init__(self,
-                 data_path="./data/mnist_data/",
-                 batch_size=60,
-                 process='train'):
-        self.batch_size = batch_size
-        self.data_path = data_path
-        self._pointer = 0
-        self.image_batches = np.array([])
-        self.process = process
-
-    def _extract_images(self, filename, n):
-        f = open(filename, 'rb')
-        f.read(16)
-        data = np.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
-        #Mapping data into [-1, 1]
-        data = data / 255. * 2. - 1
-        data_batches = np.split(data, 60000 / self.batch_size, 0)
-
-        f.close()
-
-        return data_batches
-
-    @property
-    def pointer(self):
-        return self._pointer
-
-    def load_data(self):
-        TRAIN_IMAGES = '%s/train-images-idx3-ubyte' % self.data_path
-        TEST_IMAGES = '%s/t10k-images-idx3-ubyte' % self.data_path
-
-        if self.process == 'train':
-            self.image_batches = self._extract_images(TRAIN_IMAGES, 60000)
-        else:
-            self.image_batches = self._extract_images(TEST_IMAGES, 10000)
-
-    def next_batch(self):
-        batch = self.image_batches[self._pointer]
-        self._pointer = (self._pointer + 1) % (60000 / self.batch_size)
-        return np.array(batch)
-
-    def reset_pointer(self):
-        self._pointer = 0

v1_api_demo/vae/vae_conf.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-import numpy as np
-
-is_generating = get_config_arg("is_generating", bool, False)
-
-settings(batch_size=32, learning_rate=1e-3, learning_method=AdamOptimizer())
-
-X_dim = 28 * 28
-h_dim = 128
-z_dim = 100
-
-
-def reparameterization(mu, logvar):
-    eps = ParamAttr(initial_mean=0., initial_std=1)
-    with mixed_layer() as sigma:
-        sigma += dotmul_projection(layer_math.exp(logvar) * 0.5, param_attr=eps)
-    return mu + sigma
-
-
-def q_func(X):
-    """
-    xavier initialization
-    """
-    param_attr = ParamAttr(
-        name='share.w', initial_mean=0., initial_std=1. / np.sqrt(X_dim / 2.))
-    mu_param = ParamAttr(
-        name='mu.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
-    logvar_param = ParamAttr(
-        name='logvar.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
-
-    bias_attr = ParamAttr(name='share.bias', initial_mean=0., initial_std=0.)
-    mu_bias = ParamAttr(name='mu.bias', initial_mean=0., initial_std=0.)
-    logvar_bias = ParamAttr(name='logvar.bias', initial_mean=0., initial_std=0.)
-
-    share_layer = fc_layer(
-        X,
-        size=h_dim,
-        param_attr=param_attr,
-        bias_attr=bias_attr,
-        act=ReluActivation())
-
-    return (fc_layer(
-        share_layer,
-        size=z_dim,
-        param_attr=mu_param,
-        bias_attr=mu_bias,
-        act=LinearActivation()), fc_layer(
-            share_layer,
-            size=z_dim,
-            param_attr=logvar_param,
-            bias_attr=logvar_bias,
-            act=LinearActivation()))
-
-
-def generator(z):
-
-    hidden_param = ParamAttr(
-        name='hidden.w', initial_mean=0., initial_std=1. / np.sqrt(z_dim / 2.))
-    hidden_bias = ParamAttr(name='hidden.bias', initial_mean=0., initial_std=0.)
-    prob_param = ParamAttr(
-        name='prob.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
-    prob_bias = ParamAttr(name='prob.bias', initial_mean=0., initial_std=0.)
-
-    hidden_layer = fc_layer(
-        z,
-        size=h_dim,
-        act=ReluActivation(),
-        param_attr=hidden_param,
-        bias_attr=hidden_bias)
-    prob = fc_layer(
-        hidden_layer,
-        size=X_dim,
-        act=SigmoidActivation(),
-        param_attr=prob_param,
-        bias_attr=prob_bias)
-
-    return prob
-
-
-def reconstruct_error(prob, X):
-    cost = multi_binary_label_cross_entropy(input=prob, label=X)
-    return cost
-
-
-def KL_loss(mu, logvar):
-    with mixed_layer() as mu_square:
-        mu_square += dotmul_operator(mu, mu, scale=1.)
-
-    cost = 0.5 * sum_cost(layer_math.exp(logvar) + mu_square - 1. - logvar)
-
-    return cost
-
-
-if not is_generating:
-    x_batch = data_layer(name='x_batch', size=X_dim)
-    mu, logvar = q_func(x_batch)
-    z_samples = reparameterization(mu, logvar)
-    prob = generator(z_samples)
-    outputs(reconstruct_error(prob, x_batch) + KL_loss(mu, logvar))
-else:
-    z_samples = data_layer(name='noise', size=z_dim)
-    outputs(generator(z_samples))

v1_api_demo/vae/vae_train.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import random
-import numpy as np
-import cPickle
-import sys, os
-from PIL import Image
-
-from paddle.trainer.config_parser import parse_config
-from paddle.trainer.config_parser import logger
-import py_paddle.swig_paddle as api
-import dataloader
-import matplotlib.pyplot as plt
-
-
-def plot_samples(samples):
-    fig = plt.figure(figsize=(4, 4))
-    gs = gridspec.GridSpec(4, 4)
-    gs.update(wspace=0.05, hspace=0.05)
-    for i, sample in enumerate(samples):
-        plt.subplot(gs[i])
-        plt.axis('off')
-        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
-
-    return fig
-
-
-def CHECK_EQ(a, b):
-    assert a == b, "a=%s, b=%s" % (a, b)
-
-
-def get_fake_samples(generator_machine, batch_size, noise):
-    gen_inputs = api.Arguments.createArguments(1)
-    gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
-    gen_outputs = api.Arguments.createArguments(0)
-    generator_machine.forward(gen_inputs, gen_outputs, api.PASS_TEST)
-    fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
-    return fake_samples
-
-
-def copy_shared_parameters(src, dst):
-    '''
-    copy the parameters from src to dst
-    :param src: the source of the parameters
-    :type src: GradientMachine
-    :param dst: the destination of the parameters
-    :type dst: GradientMachine
-    '''
-    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
-    src_params = dict([(p.getName(), p) for p in src_params])
-
-    for i in xrange(dst.getParameterSize()):
-        dst_param = dst.getParameter(i)
-        src_param = src_params.get(dst_param.getName(), None)
-        if src_param is None:
-            continue
-        src_value = src_param.getBuf(api.PARAMETER_VALUE)
-        dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
-        CHECK_EQ(len(src_value), len(dst_value))
-        dst_value.copyFrom(src_value)
-        dst_param.setValueUpdated()
-
-
-def find(iterable, cond):
-    for item in iterable:
-        if cond(item):
-            return item
-    return None
-
-
-def get_layer_size(model_conf, layer_name):
-    layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
-    assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
-    return layer_conf.size
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--use_gpu", default="1", help="1 means use gpu for training")
-    parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
-    args = parser.parse_args()
-    use_gpu = args.use_gpu
-    assert use_gpu in ["0", "1"]
-
-    if not os.path.exists("./samples/"):
-        os.makedirs("./samples/")
-
-    if not os.path.exists("./params/"):
-        os.makedirs("./params/")
-
-    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
-                   '--log_period=1000', '--gpu_id=' + args.gpu_id,
-                   '--save_dir=' + "./params/")
-
-    conf = "vae_conf.py"
-
-    trainer_conf = parse_config(conf, "is_generating=False")
-    gener_conf = parse_config(conf, "is_generating=True")
-
-    batch_size = trainer_conf.opt_config.batch_size
-
-    noise_dim = get_layer_size(gener_conf.model_config, "noise")
-
-    mnist = dataloader.MNISTloader(batch_size=batch_size)
-    mnist.load_data()
-
-    training_machine = api.GradientMachine.createFromConfigProto(
-        trainer_conf.model_config)
-
-    generator_machine = api.GradientMachine.createFromConfigProto(
-        gener_conf.model_config)
-
-    trainer = api.Trainer.create(trainer_conf, training_machine)
-
-    trainer.startTrain()
-
-    for train_pass in xrange(100):
-        trainer.startTrainPass()
-        mnist.reset_pointer()
-        i = 0
-        it = 0
-        while mnist.pointer != 0 or i == 0:
-            X = mnist.next_batch().astype('float32')
-
-            inputs = api.Arguments.createArguments(1)
-            inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(X))
-
-            trainer.trainOneDataBatch(batch_size, inputs)
-
-            if it % 1000 == 0:
-
-                outputs = api.Arguments.createArguments(0)
-                training_machine.forward(inputs, outputs, api.PASS_TEST)
-                loss = np.mean(outputs.getSlotValue(0).copyToNumpyMat())
-                print "\niter: {}".format(str(it).zfill(3))
-                print "VAE loss: {}".format(str(loss).zfill(3))
-
-                #Sync parameters between networks (GradientMachine) at the beginning
-                copy_shared_parameters(training_machine, generator_machine)
-
-                z_samples = np.random.randn(batch_size,
-                                            noise_dim).astype('float32')
-                samples = get_fake_samples(generator_machine, batch_size,
-                                           z_samples)
-
-                #Generating the first 16 images for a picture. 
-                figure = plot_samples(samples[:16])
-                plt.savefig(
-                    "./samples/{}_{}.png".format(
-                        str(train_pass).zfill(3), str(i).zfill(3)),
-                    bbox_inches='tight')
-                plt.close(figure)
-                i += 1
-            it += 1
-
-        trainer.finishTrainPass()
-    trainer.finishTrain()
-
-
-if __name__ == '__main__':
-    main()